System and method for extracting a region of interest from volume data

ABSTRACT

The present disclosure relates to a system and method for extracting a region of interest. Image data in a first sectional plane may be acquired. The image data in the first sectional plane may include at least one first slice image and one second slice image. A first region of interest (ROI) in the first slice image may be determined. A second ROI in the second slice image may be determined. A first volume of interest (VOI) may be determined based on the first ROI, the second ROI, and characteristic information of the image data in the first sectional plane.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a continuation of International ApplicationNo. PCT/CN2017/095320, filed on Jul. 31, 2017, the contents of which arehereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to a system and method forimage processing, and more specifically, relates to an interactivesystem and method for extracting a region of interest from volume data.

BACKGROUND

With the development of science and technology, medical images arewidely used in clinical detection and diagnosis. Medical images of highquality contribute to accurately locating lesions and help improve theaccuracy of diagnosis. During a process of detection and diagnosis(e.g., segmentation of a liver, detection of a tumor, a surgicalanalysis, etc.), usually tissue may need to be marked in images, graylevel information of a volume of interest (VOI) may be extracted, andinformation relating to the tissue and/or VOI may be displayed on athree-dimensional image for observation. Currently, a commonly usedmethod is to draw a contour outline of a regions of interest (ROI) on atwo-dimensional image, expand the ROI to a three-dimensional VOIaccording to the contour outline, and then display the results. Existingmethods do not allow editing the VOI directly on a three-dimensionalimage or allow a user to directly understand the effect on the wholeextraction process of the VOI caused by the drawing of the contouroutlines of ROIs in different slices. Therefore, it may be difficult forthe generated VOI to be satisfactory to a user.

Furthermore, during the process of extracting an ROI, a region growingalgorithm is commonly used to perform image segmentation. In athree-dimensional image, a spatial relationship is present betweendifferent tissues. When region growing is performed on tissue (e.g.,blood vessel(s)), real-time results may be masked by other tissue (e.g.,skeleton(s)), which can make it inconvenient for a user to observe.Hence, the present disclosure provides a method that can facilitate auser to interact with an image processing system. The method can enablea user to observe and/or adjust the real-time extraction result(s)conveniently during a full-automatic, semi-automatic or manualextraction of an ROI.

SUMMARY

In one aspect of the present disclosure, a method for extracting aregion of interest is provided. The method may be implemented on atleast one machine, and each of the at least one machine may have atleast one processor and one storage. The method may include: acquiringimage data in a first sectional plane, the image data in the firstsectional plane including at least one first slice image and one secondslice image; determining a first region of interest (ROI) in the firstslice image; determining a second ROI in the second slice image; anddetermining, based on the first ROI, the second ROI, and characteristicinformation of the image data in the first sectional plane, a firstvolume of interest (VOI).

In some embodiments, the image data in the first sectional plane mayinclude image data in a transverse plane, image data in a coronal plane,or image data in a sagittal plane.

In some embodiments, the method may further include: displaying thefirst ROI or the second ROI in a two-dimensional reconstruction view,and displaying the first VOI synchronously in a three-dimensionalreconstruction view; and displaying the first ROI or the second ROI inthe first VOI displayed in the three-dimensional reconstruction view.

In some embodiments, the determining a first VOI may include:determining, based on the characteristic information, whether the firstROI, the second ROI, or the first VOI satisfies a pre-determinedcondition; in response to a determination that the first ROI, the secondROI, or the first VOI does not satisfy the pre-determined condition:editing the first ROI or the second ROI; and generating, based on theedited first ROI or the edited second ROI, an edited first VOI.

In some embodiments, the method may further include: determining a firstcontour line of the first ROI, the first contour line including at leastone first control point; determining a second contour line of the secondROI, the second contour line including at least one second controlpoint; displaying the first contour line or the second contour line inthe first VOI; and editing the at least one first control point of thefirst contour line or the at least one second control point of thesecond contour line in the first VOI to obtain an edited first ROI or anedited second ROI, and an edited first VOI.

In some embodiments, the pre-determined condition may relate to whetherthe first ROI, the second ROI, or the first VOI includes at least one ofa blood vessel, calcified tissue, or fracture tissue.

In some embodiments, the method may further include: generating a firstcurve in the first slice image, wherein the first curve includes atleast one first control point, and the first curve divides the first ROIinto at least two regions; and generating a second curve in the secondslice image, wherein the second curve includes at least one secondcontrol point, and the second curve divides the second ROI into at leasttwo regions.

In some embodiments, the method may further include: generating, basedon the at least one first control point of the first curve and the atleast one second control point of the second curve, a first curvedsurface using an interpolation algorithm, the first curved surfacedividing the first VOI into at least two portions.

In some embodiments, the method may further include: displaying thefirst curve or the second curve in a multiplanar reconstruction window;and synchronously displaying the first curved surface, the first curve,or the second curve in a volume rendering window.

In some embodiments, the method may further include: optimizing, basedon the characteristic information of the image data, the first VOI toobtain a second VOI, the second VOI including at least one portion ofthe first VOI.

In some embodiments, the first VOI may include a third VOI, and themethod may further include: performing, based on the first VOI, regiongrowing of the third VOI at a first point in time; suspending regiongrowing of the third VOI at a second point in time; determining, basedon depth information of the image data and the first VOI, at least oneportion of the third VOI, wherein the at least one portion of the thirdVOI includes at least one first voxel, and a depth relating to the firstvoxel is less than or equal to a depth relating to the image data;determining, based on the at least one portion of the third VOI, a firsttexture, the first texture including gray level distribution informationof the at least one first voxel; and determining, based on the firsttexture and the first VOI, a second texture, the second textureincluding the first texture.

In some embodiments, the characteristic information may include graylevel information.

In another aspect of the present disclosure, a method for extracting aregion of interest is provided. The method may be implemented on atleast one machine, and each of the at least one machine may have atleast one processor and one storage. The method may include: acquiringimage data in a first sectional plane, the image data in the firstsectional plane including at least one first slice image and a secondslice image; determining, based on the first slice image, a first set ofcontrol points, the first set of control points including at least twocontrol points; determining, based on the second slice image, a secondset of control points, the second set of control points including atleast two control points; determining, based on the first set of controlpoints, a first spline curve; determining, based on the second set ofcontrol points, a second spline curve; generating, based on the firstspline curve and the second spline curve, a first curved surface;editing, based on the first curved surface, the first spline curve orthe second spline curve; and generating, based on the edited firstspline curve or the edited second spline curve, a second curved surface.

In some embodiments, the image data in the first sectional plane mayinclude image data in a transverse plane, image data in a coronal plane,or image data in a sagittal plane.

In some embodiments, the editing the first spline curve or the secondspline curve may include one or more of the following operations.

At least one control point of the first set of control points or atleast one control point of the second set of control points may beadjusted based on the first curved surface. At least one control pointof the first set of control points may be adjusted based on the firstspline curve. At least one control point of the second set of controlpoints may be adjusted based on the second spline curve.

In some embodiments, the editing the first spline curve or the secondspline curve may include one or more of the following operations.

The first spline curve or the second spline curve may be edited based oncharacteristic information of the image data in the first sectionalplane.

In some embodiments, the method may further include: displaying thefirst spline curve or the second spline curve in a multiplanarreconstruction window; and synchronously displaying the first curvedsurface, the first spline curve, or the second spline curve in a volumerendering window or a mesh rendering window.

In some embodiments, the method may further include: adjusting, based onthe first curved surface, at least one control point of the first set ofcontrol points or at least one control point of the second set ofcontrol points in the volume rendering window or the mesh renderingwindow.

In another aspect of the present disclosure, a method for extracting aregion of interest is provided. The method may be implemented on atleast one machine, and each of the at least one machine may have atleast one processor and one storage. The method may include: acquiringimage data; generating, based on the image data, a first image usingthree-dimensional reconstruction, the first image including a firstvolume of interest (VOI), the first VOI including at least one firstvoxel; performing, based on the first image, region growing of the firstVOI at a first point in time; suspending region growing of the first VOIat a second point in time; determining, based on depth information ofthe image data, a second VOI, wherein the second VOI includes at leastone portion of the first VOI, the second VOI includes at least onesecond voxel, and a depth relating to the second voxel is less than orequal to a depth relating to the image data; generating, based on thesecond VOI, a first texture, the first texture including gray leveldistribution information of the at least one second voxel; anddetermining, based on the first texture and the first image, a secondtexture, the second texture including the first texture.

In some embodiments, the determining a second VOI may include:determining a first set of seed points, the first set of seed pointsincluding all the seed points growing from the first point in time tothe second point in time; determining a second set of seed points,wherein the first set of seed points including the second set of seedpoints, and wherein a depth relating to the second set of seed points isless than or equal to the depth relating to the image data; determining,based on a plurality of three-dimensional coordinates of the second setof seed points, a plurality of two-dimensional projection coordinates ofthe second set of seed points; and determining, based on the pluralityof two-dimensional projection coordinates of the second set of seedpoints, the second VOI.

In some embodiments, the determining a second VOI may further include:generating a third texture of the first VOI without considering thedepth information of the image data, which includes: determining a thirdset of seed points at a third point in time, the third set of seedpoints including at least one portion of a plurality of seed pointsgrowing from the first point in time to the third point in time;determining, based on a plurality of three-dimensional coordinates ofthe third set of seed points, a plurality of two-dimensional projectioncoordinates of the third set of seed points; and determining, based onthe plurality of two-dimensional projection coordinates of the third setof seed points, the third texture of the first VOI, the third textureincluding gray level distribution information of the at least one firstvoxel.

In some embodiments, the region growing of the first VOI may include:determining a number of extraction times of a plurality of seed pointsduring the region growing from the first point in time to the fourthpoint in time; determining whether the number of extraction times of theplurality of seed points is less than or equal to a pre-determinedvalue; in response to a determination that the number of extractiontimes of the plurality of seed points is less than or equal to thepre-determined value, decreasing a speed of generating a plurality ofnew seed points; and in response to a determination that the number ofextraction times of the plurality of seed points is more than thepre-determined value, increasing the speed of generating the pluralityof new seed points.

In another aspect of the present disclosure, a system for extracting aregion of interest is provided. The system may include at least oneprocessor, and a storage configured to store instructions. Theinstructions, when executed by the at least one processor, may cause thesystem to effectuate a method. The method may include: acquiring imagedata in a first sectional plane, the image data in the first sectionalplane including at least one first slice image and one second sliceimage; determining a first region of interest (ROI) in the first sliceimage; determining a second ROI in the second slice image; anddetermining, based on the first ROI, the second ROI, and characteristicinformation of the image data in the first sectional plane, a firstvolume of interest (VOI).

In another aspect of the present disclosure, a non-transitorycomputer-readable medium is provided. The non-transitorycomputer-readable medium may include executable instructions. Whenexecuted by at least one processor, the executable instructions maycause the at least one processor to effectuate a method. The method mayinclude: acquiring image data in a first sectional plane, the image datain the first sectional plane including at least one first slice imageand one second slice image; determining a first region of interest (ROI)in the first slice image; determining a second ROI in the second sliceimage; and determining, based on the first ROI, the second ROI, andcharacteristic information of the image data in the first sectionalplane, a first volume of interest (VOI).

In another aspect of the present disclosure, a system for extracting aregion of interest is provided. The system may include at least oneprocessor, and a storage configured to store instructions. Theinstructions, when executed by the at least one processor, may cause thesystem to effectuate a method. The method may include: acquiring imagedata in a first sectional plane, the image data in the first sectionalplane including at least one first slice image and a second slice image;determining, based on the first slice image, a first set of controlpoints, the first set of control points including at least two controlpoints; determining, based on the second slice image, a second set ofcontrol points, the second set of control points including at least twocontrol points; determining, based on the first set of control points, afirst spline curve; determining, based on the second set of controlpoints, a second spline curve; generating, based on the first splinecurve and the second spline curve, a first curved surface; editing,based on the first curved surface, the first spline curve or the secondspline curve; and generating, based on the edited first spline curve orthe edited second spline curve, a second curved surface.

In another aspect of the present disclosure, a non-transitorycomputer-readable medium is provided. The non-transitorycomputer-readable medium may include executable instructions. Whenexecuted by at least one processor, the executable instructions maycause the at least one processor to effectuate a method. The method mayinclude: acquiring image data in a first sectional plane, the image datain the first sectional plane including at least one first slice imageand a second slice image; determining, based on the first slice image, afirst set of control points, the first set of control points includingat least two control points; determining, based on the second sliceimage, a second set of control points, the second set of control pointsincluding at least two control points; determining, based on the firstset of control points, a first spline curve; determining, based on thesecond set of control points, a second spline curve; generating, basedon the first spline curve and the second spline curve, a first curvedsurface; editing, based on the first curved surface, the first splinecurve or the second spline curve; and generating, based on the editedfirst spline curve or the edited second spline curve, a second curvedsurface.

In another aspect of the present disclosure, a system for extracting aregion of interest is provided. The system may include at least oneprocessor, and a storage configured to store instructions. Theinstructions, when executed by the at least one processor, may cause thesystem to effectuate a method. The method may include: acquiring imagedata; generating, based on the image data, a first image usingthree-dimensional reconstruction, the first image including a firstvolume of interest (VOI), the first VOI including at least one firstvoxel; starting, based on the first image, region growing of the firstVOI at a first point in time; suspending region growing of the first VOIat a second point in time; determining, based on depth information ofthe image data, a second VOI, wherein the second VOI includes at leastone portion of the first VOI, the second VOI includes at least onesecond voxel, and a depth relating to the second voxel is less than orequal to a depth relating to the image data; drawing, based on thesecond VOI, a first texture, the first texture including gray leveldistribution information of the at least one second voxel; anddetermining, based on the first texture and the first image, a secondtexture, the second texture including the first texture.

In another aspect of the present disclosure, a non-transitorycomputer-readable medium is provided. The non-transitorycomputer-readable medium may include executable instructions. Whenexecuted by at least one processor, the executable instructions maycause the at least one processor to effectuate a method. The method mayinclude: acquiring image data; generating, based on the image data, afirst image using three-dimensional reconstruction, the first imageincluding a first volume of interest (VOI), the first VOI including atleast one first voxel; starting, based on the first image, regiongrowing of the first VOI at a first point in time; suspending regiongrowing of the first VOI at a second point in time; determining, basedon depth information of the image data, a second VOI, wherein the secondVOI includes at least one portion of the first VOI, the second VOIincludes at least one second voxel, and a depth relating to the secondvoxel is less than or equal to a depth relating to the image data;drawing, based on the second VOI, a first texture, the first textureincluding gray level distribution information of the at least one secondvoxel; and determining, based on the first texture and the first image,a second texture, the second texture including the first texture.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide further understandingof the present disclosure and serve as a part of the present disclosure.The exemplary embodiments and relevant descriptions are for the purposeof illustration, and not intended to limit the present disclosure. Thesame reference numerals represent the same structures throughout thedrawings, and wherein:

FIG. 1 is a schematic diagram illustrating an application scenario of anexemplary image processing system according to some embodiments of thepresent disclosure;

FIG. 2 is a schematic diagram illustrating the structure of an exemplarycomputing device according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating an exemplary data processingengine according to some embodiments of the present disclosure;

FIG. 4 is a schematic diagram illustrating an exemplary VOIdetermination module according to some embodiments of the presentdisclosure;

FIG. 5 is a flowchart of an exemplary process for image processingaccording to some embodiments of the present disclosure;

FIG. 6A is a flowchart of an exemplary process for determining a VOIaccording to some embodiments of the present disclosure;

FIG. 6B is a flowchart of an exemplary process for determining a VOIand/or an ROI according to some embodiments of the present disclosure;

FIG. 7 is a flowchart of an exemplary process for determining a VOIaccording to some embodiments of the present disclosure;

FIG. 8 is a flowchart of an exemplary process for generating a curvedsurface according to some embodiments of the present disclosure;

FIG. 9 is a flowchart of an exemplary process for generating and editinga curved surface in a multiplanar reconstruction window and/or a volumerendering window according to some embodiments of the presentdisclosure;

FIGS. 10A and 10B are schematic diagrams of an exemplary spline curveaccording to some embodiments of the present disclosure;

FIG. 11 is a flowchart of an exemplary process for performing regiongrowing on a VOI based on volume rendering (VR) according to someembodiments of the present disclosure;

FIG. 12 is a flowchart of an exemplary process for non-linear VOI regiongrowing according to some embodiments of the present disclosure; and

FIG. 13 is a flowchart of an exemplary process for determining a VOIbased on a VR image according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. Apparently, the drawings used in the followingdescription only illustrate some examples or embodiments of the presentdisclosure. For those having ordinary skills in the art, the presentdisclosure may be applied to other similar circumstances according tothe drawings without creative work. It should be appreciated that theexemplary embodiments are provided only to assist those skilled in theart to better understand and implement the present disclosure, notintended to limit the scope of the present disclosure in any means. Thesame numerals in the drawings represent the same structures oroperations unless apparent from the language environment or otherwiseclarified.

As used in the present disclosure and the claims, the singular forms“a,” “an,” “one” and “the” may be intended to include the plural formsas well, unless the context clearly indicates otherwise. Generally, theterms “comprises,” “comprising,” “includes,” and/or “including” whenused in the disclosure, specify the presence of stated steps andelements, but do not preclude the presence or addition of one or moreother steps and elements.

Although the present disclosure makes various references to certainmodules in the system according to some embodiments of the presentdisclosure, any number of different modules may be used and run on aclient terminal and/or a server. The modules are illustrative only, anddifferent aspects of the systems and methods may use different modules.

Flowcharts are used in the present disclosure to illustrate operationsperformed by the system according to some embodiments of the presentdisclosure. It should be understood that the preceding or followingoperations may not be necessarily performed exactly in order. Instead,various steps may be processed in reverse sequence and/orsimultaneously. Moreover, other operations may also be added into theseprocedures, or one or more steps may be removed from these procedures.

In the process of image data processing, the extraction of a region ofinterest (ROI) or volume of interest (VOI) performed by the system mayinclude information of image pixels or voxels that meet certain criteriaextracted from a relatively large area. The system may extract the ROIor VOI based on corresponding characteristic information of pixels orvoxels of an image. In some embodiments, the correspondingcharacteristic information of the pixels or voxels may include a texturestructure, a gray level, an average gray level, a signal intensity,color saturation, contrast, luminance, or the like, or any combinationthereof, associated with the image. In some embodiments, the spatiallocation characteristics of the pixels or voxels may be used for theextraction process of the ROI or VOI. It should be noted that the terms“tissue partition,” “image segmentation,” “image extraction” and “imageclassification” may represent the same operation.

It should be noted that the above descriptions of image data processingare only provided for the convenience of illustration, and not intendedto limit the present disclosure to the scope of the mentionedembodiments. It should be understand that for those skilled in the art,after understanding the principles of the system and method, the modulesmay be combined in any means or connected to other modules assub-systems. Various modifications and changes may be conducted on theform or details of the application fields of the system and method,without departing from the principles.

FIG. 1 is a schematic diagram illustrating an application scenario of anexemplary image processing system according to some embodiments of thepresent disclosure. The image processing system may include an imagingdevice 110, a data processing engine 120, a storage device 130, and aninteractive device 140. The imaging device 110, the data processingengine 120, the storage device 130 and the interactive device 140 maycommunicate with each other via a network 150.

In some embodiments, the imaging device 110 may obtain data by scanningan object. The imaging device 110 may include but is not limited tocomputed tomography (CT), computed tomography angiography (CTA),positron emission tomography (PET), single photon emission computedtomography (SPECT), magnetic resonance imaging (MRI), digitalsubtraction angiography (DSA), ultrasound scanning (US), thermal texturemaps (TTM), SPECT-MR, CT-PET, CE-SPECT, PET-MR, PET-US, SPECT-US,TMS-MR, US-CT, US-MR, X ray-CT, X ray-PET, or the like, or anycombination thereof. In some embodiments, the object for scanning may bean organ, a body, a substance, an injured part, a tumor, or the like, orany combination thereof. In some embodiments, the object for scanningmay be a head, a chest, an abdomen, an organ, skeletons, blood vessels,or the like, or any combination thereof. In some embodiments, the objectfor scanning may be vascular tissue of one or more body parts, a liver,etc. In some embodiments, the obtained data may be image data. The imagedata may be two-dimensional image data and/or three-dimensional imagedata. In a two-dimensional image, the smallest distinguishable elementmay be a pixel. In a three-dimensional image, the smallestdistinguishable element may be a voxel. In a three-dimensional image,the image may include a series of two-dimensional slices ortwo-dimensional sections. A point (or element) of an image may bereferred to as a voxel in a three-dimensional image and may be referredto as a pixel in a two-dimensional slice image where it is located. Theterms “voxel” and/or “pixel” are only for the convenience ofdescription, and not intended to limit the two-dimensional image and/orthe three-dimensional image.

The format of the image data may include but is not limited to a JointPhotographic Experts Group (JPEG) format, a Tagged Image File Format(TIFF), a Graphics Interchange Format (GIF), a Kodak Flash Pix (FPX)format, a Digital Imaging and Communications in Medicine (DICOM) format,etc. In some embodiments, the imaging device 110 may transmit theobtained data via the network 150 to the data processing engine 120, thestorage device 130 and/or the interactive device 140, etc. For instance,the image data may be transmitted to the data processing engine 120 forfurther processing, and may also be stored in the storage device 130.

The data processing engine 120 may process data. The data may includeimage data, data inputted by a user, etc. The image data may betwo-dimensional image data and/or three-dimensional image data, etc. Thedata inputted by a user may include data processing parameters (e.g.,the slice thickness, the slice gap, the number of slices, etc.),instructions associated with the system, etc. The data may be dataobtained by the imaging device 110, data read from the storage device130, data obtained from the interactive device 140, or data obtainedfrom a cloud or an external device via the network 150. In someembodiments, the processing of the data may include data acquisition,classification, screening, transformation, computation, display, or thelike, or any combination thereof. The data processing engine 120 maytransmit the processed data to the storage device 130 for storage or tothe interactive device 140. For example, the data processing engine 120may process image data and transmit the processed image data to theinteractive device 140 for display.

In some embodiments, the data processing engine 120 may include but isnot limited to a Central Processing Unit (CPU), an Application SpecificIntegrated Circuit (ASIC), an Application Specific Instruction SetProcessor (ASIP), a Physics Processing Unit (PPU), a Digital ProcessingProcessor (DSP), a Field-Programmable Gate Array (FPGA), a ProgrammableLogic Device (PLD), a processor, a microprocessor, a controller, amicrocontroller, or the like, or any combination thereof.

It should be noted that the data processing engine 120 illustrated abovemay be practically present in the system and may also implementcorresponding functions via a cloud computing platform. The cloudcomputing platform may include but is not limited to a storage cloudplatform mainly used for data storage, a computing cloud platform mainlyused for data processing, a comprehensive cloud computing platform usedfor both data storage and data processing, etc. The cloud platform foruse in the image processing system 100 may be a public cloud, a privatecloud, a community cloud or a hybrid cloud, etc. For example, somemedical images obtained by the image processing system 100 may becomputed and/or stored via a cloud platform according to practicalneeds; some other medical images may be computed and/or stored by localprocessing modules and/or a storage within the system.

The storage device 130 may be set on a device with storage functions.The storage device 130 may store data collected from the imaging device110 (e.g., data of an image taken by the imaging device 110) and variousdata generated in the work of the data processing engine 120. Thestorage device 130 may also store data inputted via the interactivedevice 140 (data inputted by a user). The storage device 130 may belocal or remote. In some embodiments, the storage device 130 may be seton the data processing engine 120. The storage device 130 may includehierarchical database, network database, relational database, or thelike, or any combination thereof. The storage device 130 may digitalizethe information and store the information using an electrical, magneticor optical storage device. The storage device 130 may be used forstoring various information, such as programs, data, etc. The storagedevice 130 may be set on a device that stores information using electricenergy, for example, a Random Access Memory (RAM), a Read Only Memory(ROM), etc. An RAM may include but is not limited to a decade countertube, a selectron tube, a delay line memory, a Williams tube, a DynamicRandom Access Memory (DRAM), a Static Random Access Memory (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. An ROM mayinclude but is not limited to a magnetic bubble memory, a twistermemory, a film storage, a magnetic plated wire memory, a magnetic corememory, a magnetic drum memory, an optical driver, a hard disk, a tape,an early Non-Volatile Random Access Memory (NVRAM), a phase changememory, a variable reluctance random access memory, a ferroelectricrandom access memory, a non-volatile SRAM, a flash memory, anelectrically erasable programmable read only memory, an erasableprogrammable read only memory, a programmable read only memory, a maskread only memory, floating gate random access memory, nanometer randomaccess memory, track memory, thyrecotor memory, programmablemetallization cell, or the like, or any combination thereof. The storagedevice 130 may be set on a device using magnetic energy to storeinformation, such as a hard disk, a soft disk, a tape, a magnetic corestorage, a magnetic bubble storage, a USB drive, a flash memory, etc.The storage device 130 may be configured as an optical storage device,for example, a compact disk (CD), a digital video disk (DVD), etc. Thestorage device 130 may be configured as a magneto-optic storage device,such as a photomagnetic disk, etc. The method of information access inthe storage device 130 may be random storage, serial access storage,read only memory, or the like, or any combination thereof. The storagedevice 130 may be set on a non-permanent memory or a permanent memory.The storage devices mentioned above are only exemplary, and not intendedto limit the storage device used in the image processing system 100.

The interactive device 140 may receive, transmit and/or display data orinformation. In some embodiments, the interactive device 140 may have apart of or all of the functions of the data processing engine 120. Forexample, the interactive device 140 may perform further processing onthe processing results of the data processing engine 120, such asdisplaying the data processed by the data processing engine 120. In someembodiments, the interactive device 140 and the data processing engine120 may be a single integrated device. The integrated device mayimplement the functions of the data processing engine 120 as well as theinteractive device 140. In some embodiments, the interactive device 140may include but is not limited to an input device, an output device, orthe like, or any combination thereof. The input device may include butis not limited to a device for inputting characters (e.g., a keyboard),an optical reading device (e.g., an optical mark reader, an opticalcharacter reader), a graphic input device (e.g., a mouse, an action bar,a light pen), an image input device (e.g., a camera, a scanner, a faxmachine), an analog input device (e.g., a lingual analog digitalconversion identification system), or the like, or any combinationthereof. The output device may include but is not limited to a displaydevice, a printing device, a plotting device, an image output system, aspeech output system, a magnetic recording device, or the like, or anycombination thereof. In some embodiments, the interactive device 140 maybe a device having input functions as well as output functions, such asa desk-top computer, a lap-top computer, a smart phone, a tabletcomputer, a personal digital assistance (PDA), etc.

The network 150 may implement the intercommunication of the imageprocessing system 100, receive information from the outside of thesystem, transmit information to the outside of the system, etc. In someembodiments, the imaging device 110, the data processing engine 120 andthe interactive device 140 may be connected to each other by wirednetwork, wireless network or a combination of both via network 150. Thenetwork 150 may be a single network or a combination of multiple typesof network. In some embodiments, the network 150 may include but is notlimited to a local area network, a wide area network, a public network,a private network, a wireless local area network, a virtual network, ametropolitan area network, a public telephone switched network, or thelike, or any combination thereof. In some embodiments, the network 150may include multiple types of network access points, such as wired orwireless network access points, base stations and/or internet exchangepoints through which data may be connected to the network 150 andinformation may be transmitted via the network 150.

It should be understand that the image processing system 100 illustratedin FIG. 1 may be implemented in various ways. For example, in someembodiments, the system 100 may be implemented by hardware, software ora combination of hardware and software. The hardware portion may beimplemented by special logic circuit, and the software may be stored ina storage and executed by a suitable instruction execution system, suchas a microcontroller or special hardware. Those skilled in the art mayunderstand that the method and system above may be implemented by usingcomputer executable instructions and/or may be implemented when embeddedin the control code of a processor. For example, such code may beprovided by a carrier medium such as a magnetic disk, a CD or a DVD-ROM,a programmable storage such as a read only memory (firmware) or a datacarrier such as an optical medium or an electronic signal medium. Thesystem and modules of the present disclosure may be implemented by, forexample, very large scale integration circuit or gate array,semiconductors such as logic chips, transistors, etc., or the hardwarecircuit of a programmable hardware device such as field programmablegate array, a programmable logic device, etc. The system and modules ofthe present disclosure may also be implemented by, for example, softwareexecuted by different types of processors or a combination of thehardware circuit and software (e.g., firmware) mentioned above.

It should be noted that the above descriptions of the image processingsystem 100 are only for the convenience of illustration, and notintended to limit the present disclosure to the scope of the exemplaryembodiments. It should be understand that for those skilled in the art,after understanding the principles of the system and method, the modulesmay be combined in any means or connected to other modules assub-systems. Various modifications and changes may be conducted on theform or details of the application fields of the system and method,without departing from the principles. For instance, the storage device130 may be configured on a cloud computing platform with the function ofdata storage, including but not limited to a public cloud, a privatecloud, a community cloud or a hybrid cloud, etc. As another example, twoor more of the imaging device 110, the data processing engine 120, thestorage device 130 and the interactive device 140 may be directlyconfigured in one device rather than communicating with each other viathe network 150. Similar variations fall within the protection scope ofthe present disclosure.

FIG. 2 is a schematic diagram illustrating the structure of an exemplarycomputing device according to some embodiments of the presentdisclosure. As depicted, the computing device 200 may include aprocessor 202, a read only memory (ROM) 204, a random access memory(RAM) 206, a communication port 208, an input/output component 210, adisk 212, an inter communication bus 214 and a user interface 216. Thecommunication bus 214 may implement the data communication between thecomponents of the computing device 200. The processor 202 may executeprogram instructions to implement the functions of the one or morecomponents, modules, units and subunits of the image processing system100 described in the present disclosure. The processor 202 may includeone or more processors. The communication port 208 may implement thedata communication between the computing device 200 and other componentsof the image processing system 100 (e.g., the imaging device 110) via,for example, the network 150. The computing device 200 may also includedifferent forms of program storage units and data storage units, such asthe disk 212, the read only memory (ROM) 204 and the random accessmemory (RAM) 206, for storing various data documents processed by acomputer or used for communication and possible program instructionsexecuted by the processor 202. The input/output component 210 mayimplement the data input/output between the computing device 200 andother components (such as the user interface 216) and/or othercomponents of the image processing system 100 (such as the storagedevice 130). The computing device 200 may transmit and receiveinformation and data via the communication port 208 from the network150.

FIG. 3 is a schematic diagram illustrating an exemplary data processingengine according to some embodiments of the present disclosure. The dataprocessing engine 120 may include an image data acquisition module 302,a pre-processing module 304, a VOI determination module 306, a storagemodule 308 and a display module 310. In some embodiments, two or more ofthe image data acquisition module 302, the pre-processing module 304,the VOI determination module 306, the storage module 308 and the displaymodule 310 may communicate with each other via the network 150. In someembodiments, two or more of the image data acquisition module 302, thepre-processing module 304, the VOI determination module 306, the storagemodule 308 and the display module 310 may communicate with each othervia the communication bus 214.

The image data acquisition module 302 may obtain image data. The imagemay be a medical image. The medical image may include a CT image, a PETimage, a SPECT image, an MRI image, an ultrasound image, or the like, orany combination thereof. The medical image may be a two-dimensionalimage and/or a three-dimensional image. In some embodiments, the imagedata acquisition module 302 may acquire image data from the imagingdevice 110, the storage device 130, the interactive device 140 and/orthe storage module 308. In some embodiments, the acquisition of theimage data may be real-time or non-real-time. In some embodiments, theacquired image data may be stored in the storage device 130, the storagemodule 308 or any other storage devices integrated in the system orseparated from the system described in the present disclosure. In someembodiments, the acquired image data may be transmitted to othermodules, units or subunits for further processing. For example, theimage data acquisition module 302 may send the image data to thepre-processing module 304 to pre-process the image data. As anotherexample, the image data acquisition module 302 may transmit the imagedata to the VOI determination module 306 for determining a VOI. Forinstance, the image data acquisition module may transmit the image datato the display module 310 for displaying the image.

The pre-processing module 304 may pre-process the image data. In someembodiments, the image data may include the image data acquired by theimage data acquisition module 302. The image data may also include theintermediate image data generated during the work of the data processingengine 120 (e.g., the intermediate image data generated in the processof determining a VOI performed by the VOI determination module 306). Thepre-processing may include initial positioning, image normalization,image reconstruction, image smoothing, image compression, imageenhancement, image registration, image fusion, image geometriccorrection, image denoising, or the like, or any combination thereof.The pre-processing operation may be implemented by using a pointoperation, a geometric operation, etc. The point operation may includeperforming operations on pixels of the image data, including additionsubtraction, multiplication and division, etc. The geometric operationmay include performing operations on the image data, includingtransformation, zoom, rotation, distortion correction, etc. In someembodiments, pre-processed image data may be transmitted to othermodules, units or sub-units for further processing. For example, thepre-processed image data may be transmitted to the VOI determinationmodule 306 for determining the VOI. As another example, thepre-processed data may be transmitted to the storage module 309 forstorage.

The VOI determination module 306 may determine one or more VOIs. In someembodiments, the VOI determination module 306 may process the image dataand reconstruct a three-dimensional image to implement stereodisplaying, editing and/or analyzing of a target of interest. A VOI mayinclude one or more voxels of interest. In some embodiments, a VOI mayinclude pixels in one or more slices of two-dimensional slice images. Insome embodiments, the VOI may include at least a portion of the targetof interest. In some embodiments, the target of interest may be a body,a substance, an injured part, a tumor, or the like, or any combinationthereof. In some embodiments, the target of interest may be a head, achest, an abdomen, a visceral organ, or the like, or any combinationthereof. In some embodiments, the target of interest may be one or morespecific organs or tissues, for example, a skeleton, a blood vessel, atrachea, a liver, or the like, or any combination thereof. In someembodiments, the VOI determination module 306 may determine one or moreVOIs automatically, semi-automatically, or manually. For example, theVOI determination module 306 may automatically extract one or more VOIsbased on one or more image segmentation algorithms. As another example,a user may determine one or more VOIs manually by the interactive device140. As another example, a user may manually modify or change thegenerated VOI, or perform other manual operations on the generated VOI.

In some embodiments, the automatic determination of the VOI(s) may beperformed based on one or more three-dimensional reconstructiontechniques. The three-dimensional reconstruction techniques may includea surface rendering algorithm, a volume rendering algorithm, and a meshrendering algorithm, etc. The surface rendering algorithm may a surfaceof a VOI. The surface rendering algorithm may segment the surface of theVOI to be determined in two-dimensional slice image(s), and then formthe surface of the VOI by geometric element interpolation, and renderand/or blank the surface of the VOI according to an illumination model,a dark model, etc. An image obtained through the surface renderingalgorithm may be displayed on the display module 310 so that a user mayeasily check the results of surface rendering. The surface renderingalgorithm may include a boundary contour line representation, a surfacerendering algorithm based on voxels, a surface representation, etc. Theboundary contour line representation (e.g., the triangular fittingsurface algorithm) may extract the contour lines in the slice image(s)based on one or more image segmentation algorithms, and then stack thecontour lines corresponding to one or more two-dimensional slice imagesto represent the surface boundary of the VOI. The surface renderingalgorithm based on voxels may generate the surface of the VOI in voxellevel. First of all, the VOI determination module 306 may extract theobject of interest from a background using a threshold segmentationmethod, and then determine the voxels constituting the surface of theVOI by deep traversal searching. For example, the voxels located on theproximal surface of the VOI and the background may constitute thesurface of the VOI. The surface representation may reconstruct thesurface based on the boundary contour lines of the VOI. For instance,the VOI determination module 306 may segment the surface of the VOI intodifferent regions based on a plurality of boundary contour lines, andfill the regions among the adjacent boundary contour lines using smallplanes (or curved surfaces) of triangles or polygons based on one ormore plane filling algorithms (e.g., a seed filling algorithm, aninjection filling algorithm, a boundary filling algorithm, etc.) so thatthe surface of the VOI may be formed. The surface representation mayinclude a cuberille algorithm, a marching cubes algorithm, and adividing cubes algorithm, etc.

As for volume rendering, the VOI determination module 306 may considereach pixel in a two-dimensional slice image as a hexahedral element(i.e., a voxel) in a three-dimensional space, cause a virtual light rayto pass through a plurality of two-dimensional slice images and analyzethe transmission, scattering and reflection effects of each voxel wherethe virtual light ray passes. As a result, comprehensive characteristicinformation of a plurality of voxels where the virtual light ray passesmay be obtained. The volume rendering algorithm may include a spatialdomain algorithm, a transformation domain algorithm, etc. The spatialdomain algorithm may directly process and display the image data, suchas a ray tracing algorithm, a splatting algorithm, a shear-deformationalgorithm, etc. The transformation domain algorithm may transform theimage data to a transformation domain and then process and display theimage. Exemplary transformation domain algorithms may include frequencydomain volume rendering, volume rendering based on wavelet, etc. Theimage data may be the image data acquired by the image data acquisitionmodule 302 or the image data pre-processed by the pre-processing module304.

In some embodiments, the determination of the VOI may further beperformed based on three-dimensional surface model reconstructiontechniques, such as a multiplanar reconstruction (MPR), a maximumintensity projection (MIP), a surface shaded display (SSD), or the like,or any combination thereof. In some embodiments, the image dataprocessed by the VOI determination module 306 may be transmitted toother modules, units or sub-units for further processing. For instance,the image data processed by the VOI determination module 306 may betransmitted to the display module 310 for display. In some embodiments,the VOI determination module 306 may determine the pixels/voxelscorresponding to a VOI based on one or more image segmentationalgorithms, and then determine the VOI using one or morethree-dimensional reconstruction techniques. The image segmentationalgorithm may be any one of the image segmentation algorithms describedin the present disclosure (e.g., a region growing).

The storage module 308 may store data from the image data acquisitionmodule 302, the pre-processing module 304, the VOI determination module306 and/or the display module 310. The storage module 308 may includestorage devices in the system (e.g., the storage device 130, the disk212, the ROM 204, the RAM 206, etc.) and/or storage devices external tothe system. The storage module 308 may be practically present in thesystem or implement the functions of data storage and data access via acloud computing platform.

The display module 310 may display image data. In some embodiments, theimage data may include the image data acquired by the image dataacquisition module 302 and/or the intermediate image data generatedduring the work of the data processing engine 120. The intermediateimage data may be the image data pre-processed by the pre-processingmodule 304, the intermediate data generated during the process ofdetermining a VOI by the VOI determination module 306 (e.g.,two-dimensional slice images of an ROI) or the VOI determined by the VOIdetermination module 306. In some embodiments, the display module 310may include a display window for two-dimensional images, a displaywindow for three-dimensional images, etc. to display the image data intwo dimensions or three dimensions. In some embodiments, the displaymodule 310 may display two-dimensional image information in the displaywindow for three-dimensional images. For instance, during the process ofdetermining the VOI, the display module 310 may display the determinedVOI in the display window for three-dimensional images or making mark(s)on the VOI and display one or more corresponding ROIs. In someembodiments, the display module 310 may respond to information from theinteractive device 140 and adjust the displayed image regions, theangles of view for display, display effects, etc. For example, a usermay drag, rotate or switch the display window of an image using theinteractive device 140 (e.g., a mouse) and observe a VOI from differentviews. As another example, a user may select a certain ROI (or VOI)using the interactive device 140 (e.g., a mouse) and then zoom in or outfor display, etc. As a further example, a user may change a slicethickness of one or more two-dimensional slice images, and the displaymodule 310 may re-display the image data according to the information ofthe adjusted slice thickness.

It should be noted that the above descriptions of the data processingengine 120 are only for the convenience of illustration, and notintended to limit the present disclosure to the scope of the exemplaryembodiments. It should be understand that for those skilled in the art,after understanding the principles of the system and method, the modulesmay be combined in any means or connected to other modules assub-systems. Various modifications and changes may be conducted on theform or details of the application fields of the system and method,without departing from the principles. In some embodiments, the imagedata acquisition module 302, the pre-processing module 304, the VOIdetermination module 306, the storage module 308 and the display module310 may be different modules implemented on one device or system. Insome embodiments, a multi-functional module may implement the functionsof two or more modules described above. For example, the image dataacquisition module 302 and the pre-processing module 304 may be twoseparate modules or integrated into one module, wherein the integratedmodule may have the function of image data acquisition as well as thefunction of pre-processing. As another example, the VOI determinationmodule 306 and the pre-processing module 304 may be two separate modulesor integrated into one module, wherein the integrated module may havethe function of pre-processing as well as the function of determiningthe VOI. In some embodiments, the display module 310 may be integratedinto the interactive device 140. In some embodiments, various modulesmay share one storage module or have their own storage modules, etc.Similar variations fall within the protection scope of the presentdisclosure.

FIG. 4 is a schematic diagram illustrating an exemplary VOIdetermination module according to some embodiments of the presentdisclosure. The VOI determination module may include an ROIdetermination module 402, a VOI generation module 404, a curvegeneration unit 406, a curved surface generation unit 408, an editingunit 410, an updating unit 412, and a judgment unit 414. In someembodiments, the ROI determination unit 402, the VOI generation unit404, the curve generation unit 406, the curved surface generation unit408, the editing unit 410, the updating unit 412 and the judgment unit414 may communicate with two or more described units or other devices ormodules of the image processing system 100 (e.g., the storage device130) via the network 150. In some embodiments, the ROI determinationunit 402, the VOI generation unit 404, the curve generation unit 406,the curved surface generation unit 408, the editing unit 410, theupdating unit 412 and the judgment unit 414 may implement thecommunication with two or more described units or the communication withother devices or modules of the image processing system 100 (e.g., thestorage device 130) via the communication bus 214.

The ROI determination unit 402 may determine one or more ROIs. An ROImay include one or more pixels with characteristic information. Thecharacteristic information may include a texture structure, a graylevel, an average grey level, a signal intensity, color saturation,contrast, luminance, or the like, or any combination thereof. The ROImay include an ROI counter line and/or pixels within the contour line.The ROI contour line may be an approximately successive curve includinga plurality of scattered pixels. The ROI contour line may be a closed ornon-closed curve. In some embodiments, the ROI may include pixelscorresponding to an organ (e.g., a blood vessel, a liver, etc.), normaltissue, a tumor, a nodule, injured tissue, calcified tissue, or thelike, or any combination thereof. In some embodiments, the ROIdetermination unit 402 may determine one or more ROI contour lines, thecharacteristic information of the ROI, etc., in one or moretwo-dimensional slice images. In some embodiments, the determined ROImay be transmitted to other modules, units or sub-units for furtherprocessing. For example, the ROI determination unit 402 may transmit thedetermined ROI to the VOI generation unit 404 for generating a VOI. Asanother example, the ROI determination unit 402 may transmit thedetermined ROI to the editing unit 410 for editing the ROI.

The VOI generation unit 404 may generate one or more VOIs. In someembodiments, the VOI may include one or more voxels with characteristicinformation. The characteristic information may include a texturestructure, a gray level, an average gray level, a signal intensity,color saturation, contrast, luminance, or the like, or any combinationthereof. The VOI may include voxels corresponding to an organ (e.g., ablood vessel, a liver, etc.), normal tissue, a tumor, a nodule, injuredtissue or calcified tissue, or the like, or any combination thereof. TheVOI may include a contour surface and/or voxels within the contoursurface. The VOI contour surface may be an approximately successivecurved surface including a plurality of scattered voxels. The VOIcontour surface may be a closed or a non-closed curved surface. In someembodiments, the VOI generation unit 404 may determine the VOI contoursurface, the characteristic information of the voxels of the VOI, etc.The characteristic information of the voxels of the VOI may bedetermined based on the characteristic information of pixels in one ormore slice images. For example, an interpolation algorithm may beperformed on the pixels in multiple slice images to obtain voxels andcorresponding characteristic information.

In some embodiments, the VOI generation unit 404 may generate one ormore VOIs automatically, semi-automatically or via a manual input of auser. For example, the VOI generation unit 404 may extract one or moreVOIs automatically based on one or more image segmentation algorithms.As another example, a user may manually sketch the contour lines of theVOI via the interactive device 140. As a different example, a user maymanually sketch the contour lines of one or more ROIs via theinteractive device 140 so that the VOI generation unit 404 may generatethe VOI based on the contour lines of the ROI. As another example, auser may manually modify or change the generated VOI. In someembodiments, the VOI generation unit 404 may generate a VOI based on atleast two counter lines of the ROI. In some embodiments, the generatedVOI may be transmitted to other modules, units or sub-units for furtherprocessing. For example, the VOI generation unit 404 may transmit thegenerated VOI to the judgment unit 414 for determining whether the VOIsatisfies a pre-determined condition. As another example, the VOIgeneration unit 404 may transmit the generated VOI to the editing unit410 for editing or optimizing the VOI.

The curve generation unit 406 may generate one or more spline curves. Aspline curve may include one or more control points. The control pointsmay refer to points for determining a general shape and a general trendof the spline curve. A control point may be a pixel in a two-dimensionalslice image or a pixel in an image generated after an interpolationconducted on the pixels in one or more two-dimensional slice images. Thespline curve may be a curve obtained according to the interpolationfitting of a plurality of control points. The spline curve may be acontinuous curve. In some embodiments, one or more control points of aspline curve may be located in different two-dimensional images. Thespline curve may be a two-dimensional curve in a certain plane of athree-dimensional image (e.g., a traverse plane, a coronal plane, asagittal plane or a plane with any inclination angle in thethree-dimensional space), or a three-dimensional curve that spans aplurality of planes. In some embodiments, the spline curve may be aclosed curve. For instance, two endpoints of the spline curve maycoincide each other or the distance between the two endpoints is withina pre-determined threshold scope. In some embodiments, the spline curvemay be a non-closed curve. For instance, the distance between twoendpoints of the spline curve is beyond a pre-determined threshold.

In some embodiments, the curve generation unit 406 may generate a closedspline, for example, a counter line of an ROI. In some embodiments, thecurve generation unit 406 may generate a non-closed spline, forinstance, a segmentation line that segments one ROI into at least twoportions. In some embodiments, the spline curve may be drawn manually.For example, a user may manually draw a counter line of an ROI or asegmentation line of an ROI via the interactive device 140. As anotherexample, a user may determine one or more control points in an image viathe interactive device 140, and then the curve generation unit 406 maygenerate a corresponding spline curve based on the control points. Insome embodiments, the spline curve may be drawn automatically. Forinstance, the curve generation unit 406 may automatically detect acontour (or boundary) of an ROI or a segmentation line of differentregions within an ROI based on characteristic information of ROI pixelsextracted by the ROI determination unit 402. In some embodiments, thecurve generation unit 406 may determine one or more control points basedon characteristic information of an image, and then generate a splinecurve based on the control points. In some embodiments, the generatedspline curve may be further processed by the ROI determination unit 402.For instance, the ROI determination unit 402 may extract characteristicinformation of an ROI based on the spline curve generated by the curvegeneration unit 406.

The curved surface generation unit 408 may generate one or more curvedsurfaces. A curved surface may be displayed in the form of a mesh or apolygon mesh including one or more elements, for example, one or morevertices, one or more edges, one or more faces defining the shape of apolyhedral object, etc. The curved surface may be a flat face (e.g., allthe elements in the curved surface may be in the same plane) or a curvedface. The curved surface may include a closed curved surface or anon-closed curved surface. The closed curved surface may be a face (or acontour surface) of a VOI. The non-closed curved surface may be asegmentation face that segments one VOI into at least two portions. Insome embodiments, the curved surface generation unit 408 may generate acurved surface based on one or more spline curves. For instance, thecurved surface generation unit 408 may generate a mask based on one ormore spline curves, and then transform the mask into a mesh.Specifically, a mesh may be generated based on the one or more splinecurves by an interpolation among the curves, wherein the mesh may be amask image, i.e., the gray level of pixels or voxels in the mesh is 1and the gray level of pixels or voxels outside the mesh is 0. The curvedsurface generation unit 408 may further segment the mask into normativemesh structures, wherein the mesh structures may include a plurality ofmesh points. Then, the curved surface generation unit 408 may determinewhether the mesh points are within a pre-determined scope of controlpoints on the one or more spline curves. If the mesh points are withinthe pre-determined scope, then the mesh points may belong to the mesh;if the mesh points are beyond the pre-determined scope, then the meshpoints may not belong to the mesh. In some embodiments, the curvedsurface generation unit 408 may further adjust the location(s) of themesh point(s) to locate the mesh points in the pre-determined scope.

In some embodiments, the curved surface generation unit 408 may generatea closed curve surface, for example, a contour surface of a VOI. In someembodiments, the curved surface generation unit 408 may generate anon-closed curved surface, for example, a segmentation face of differentregions in a VOI. In some embodiments, the non-closed curved surface maysegment the target of interest into at least two portions. For instance,a user may need to segment a VOI (e.g., a liver) based on functions ofthe VOI, wherein the user may sketch a spline curve in each slice oftwo-dimensional slice images (e.g., two-dimensional images in a traverseplane, a sagittal plane or a coronal plane) of a liver, and the splinecurve may segment the liver region into at least two portions in thecurrent slice image, and then the curved surface generation unit 408 maygenerate a mesh based on a plurality of sketched spline curves. In athree-dimensional image, the curved surface may segment a liver into atleast two portions. In some embodiments, the generated mesh may befurther processed by the VOI generation unit 404. For instance, the VOIgeneration unit 404 may extract characteristic information of a VOIbased on the mesh generated by the curved surface generation unit 408.

The editing unit 410 may edit intermediate image data generated duringthe working process of the VOI determination module 306. Theintermediate image data may include an ROI determined by the ROIdetermination unit 402, a spline curve generated by the curve generationunit 406, a VOI generated by the VOI generation unit 404 and/or a meshgenerated by the curved surface generation unit 408, etc. The process ofthe editing may be implemented manually or automatically. For example, auser may adjust the contour line of an ROI via the interactive device140 (e.g., a mouse). As another example, the editing unit 410 mayautomatically adjust or optimize an ROI contour line based oncharacteristic information of an image. The process of the editing maybe performed based on a two-dimensional view (or a two-dimensionalimage) or a three-dimensional view (or a three-dimensional image). Forexample, determined ROI contour line(s) may be edited in atwo-dimensional view. As another example, the ROI contour line(s) of aVOI or the contour surface of a VOI may be edited in a three-dimensionalview. In some embodiments, the edited ROI or VOI may be furtherprocessed by other modules, units or sub-units. For example, the editingunit 410 may provide the edited ROI or VOI to the updating unit 412 forupdating the determined ROI or the generated VOI. As another example,the editing unit 410 may provide the edited ROI or VOI to the judgmentunit 414 for determining whether to continue editing the edited ROI orVOI.

The updating unit 412 may update intermediate image data generatedduring the working process of the VOI determination module 406. In someembodiments, the intermediate image data may be an ROI determined by anROI determination unit 402, a spline curve generated by the curvegeneration unit 406, a VOI generated by the VOI generation unit 404and/or a curved surface generated by the curved surface generation unit408, etc. In some embodiments, after edited by the editing unit 410, theROI determined by the ROI determination unit 402 or the VOI generated bythe VOI generation unit 404 may be used by the updating unit 412 forgenerating a new ROI or VOI. In some embodiments, after edited by theediting unit 410, the curve generated by the curve generation unit 406or the curved surface generated by the curved surface generation unit408 may be used by the updating unit 412 for generating a new curve orcurved surface. In some embodiments, the updating unit 412 may providethe updated curve or curved surface to the ROI determination unit 402for re-determining an ROI or to the VOI generation unit 404 forre-generating a VOI.

The judgment unit 414 may conduct a judgment on intermediate image datagenerated during the working process of the VOI determination module306. In some embodiments, the intermediate image data may include an ROIdetermined by the ROI determination unit 402, a spline curve generatedby the curve generation unit 406, a VOI generated by the VOI generationunit 404 and/or a curved surface generated by the curved surfacegeneration unit 408, etc. For instance, the judgment unit 414 mayconduct a judgment on whether the ROI determined by the ROIdetermination unit 402 (or the VOI generated by the VOI generation unit404) satisfies a pre-determined condition or user requirement(s). Thepre-determined condition may be pre-determined by the system or a user.The pre-determined condition may relate to whether the ROI or VOIincludes an organ (e.g., a blood vessel), a tumor, injured tissue, etc.The user requirement(s) may include whether the ROI (or VOI) isconsidered as suitable by a user, whether the ROI (or VOI) needs to beedited, adjusted or optimized, etc. In some embodiments, the judgmentunit 414 may provide the judging results to other modules, units orsub-units for further processing. For instance, if the judgment unit 414determines that the curved surface generated by the curved surfacegeneration unit 408 does not satisfy the user requirement(s), then thesystem may automatically adjust (or a user may manually adjust) thespline curve, and the curved surface generation unit 408 may re-generatea curved surface based on the adjusted spline curve.

In some embodiments, the ROI determined by the ROI determination unit402, the VOI generated by the VOI generation unit 404, the curvegenerated by the curve generation unit 406 and/or the curved surfacegenerated by the curved surface generation unit 408, the real-timeediting results or relevant intermediate image data of the editing unit410 may be displayed by the display module 310. For example, when a useris sketching a contour line of an ROI in a two-dimensional slice imagein a display window for two-dimensional images (e.g., a traversemultiplanar reconstruction window), the display module 310 may conductspatial orientation on the ROI and display other sectional planes (e.g.,a coronal plane, a sagittal plane) in the display window fortwo-dimensional images, or display marks of the ROI at a correspondinglocation of the ROI in the image in the display window forthree-dimensional images (e.g., a volume rendering window). As anotherexample, when a user is sketching a contour line of an ROI in each sliceimage of a plurality of two-dimensional slice images, the VOI generationunit 404 may generate a VOI based on at least two sketched ROIs.Furthermore, the display module 310 may display the generated VOI in thedisplay window for three-dimensional images in real-time.

It should be noted that the above descriptions of the VOI determinationmodule 306 are only for the convenience of illustration, and notintended to limit the present disclosure to the scope of the exemplaryembodiments. It should be understand that for those skilled in the art,after understanding the principles of the system and method, the modulesmay be combined in any means or connected to other modules assub-systems. Various modifications and changes may be conducted on theform or details of the application fields of the system and method,without departing from the principles. In some embodiments, two or moreof the ROI determination module 402, the curve generation unit 406, theVOI generation module 404, the curved surface generation unit 408, theediting unit 410, the updating unit 412 and the judgment unit 414 may bedifferent units implemented in one device or module, or may beintegrated into one unit that may implement the functions of the two ormore units. For instance, the editing unit 410 and the updating unit 412may be two separate units, or may be integrated into one unit having anediting function as well as an updating function. As another example,the ROI determination unit 402 and the VOI generation unit 404 may shareone editing unit 410, updating unit 412 or judgment unit 414, or mayhave respective editing units, updating units or judgment units. As afurther example, the ROI determination unit 402 and the curve generationunit 406 may be integrated into a single unit, while the VOI generationunit 404 and the curved surface generation unit 408 may be integratedinto a single unit. Similar variations fall within the protection scopeof the present disclosure.

FIG. 5 is a flowchart of an exemplary process for image processingaccording to some embodiments of the present disclosure. The imageprocessing process 500 may include acquiring image data in 502,pre-processing the image data in 504 and determining VOI(s) based on theimage data in 506. In some embodiments, the determining VOI(s) based onthe image data in 506 may further refer to descriptions of FIG. 6 in thepresent disclosure.

In 502, image data may be acquired. In some embodiments, the operationof acquiring image data may be performed by the image data acquisitionmodule 302. In some embodiments, the image data may be one or moretwo-dimensional slice images acquired by scanning an object. The scannedobject may be an organ, a body, a substance, an injured part, a tumor,or the like, or any combination thereof. In some embodiments, thescanned object may be a head, a chest, an abdomen, an organ (such as askeleton, a blood vessel, etc.), or the like, or any combinationthereof. In some embodiments, the image data may be two-dimensionalslice image sequence(s) within a reconstruction scope obtained from thetwo-dimensional slice image(s), or two-dimensional slice imagesequence(s) corresponding to a target of interest. The scanned objectmay include the target of interest. The target of interest may be aportion of the scanned object. For example, when performing liversegmentation, the image data may be a plurality of two-dimensional sliceimages acquired by scanning a chest and/or an abdomen, and the target ofinterest may be the liver. As another example, the image data may betwo-dimensional slice image sequence(s) corresponding to a liver, andthe target of interest may be abnormal tissue in the liver (e.g., atumor, etc.).

In some embodiments, the image data may be the image data of othersectional planes (e.g., sagittal plane, coronal plane) obtained byperforming a three-dimensional reconstruction technique based on one ormore two-dimensional slice images acquired by scanning a target. Thethree-dimensional reconstruction technique may be multiplanarreconstruction (MPR). The MPR may stack a plurality of axial images(e.g., traverse images) within the scanning scope, and then performimage reformation on a specified tissue or in a specified scope in thecoronal plane, the sagittal plane or an oblique plane with any angle, sothat a new slice image in coronal plane, sagittal plane or oblique planewith any angle may be generated.

In 504, the image data acquired in 502 may be pre-processed. In someembodiments, the operation of pre-processing may be performed by thepre-processing module 304. In some embodiments, the pre-processing mayinclude preliminary orientation, enhancement, interpolation processing,morphological processing, noise removal, or the like, or any combinationthereof. The preliminary orientation may determine a general regionwhere an ROI is located based on the image data, and thus the subsequentprocess of determining the ROI may be simplified. The preliminaryorientation may be performed automatically, semi-automatically ormanually. The enhancement processing may highlight one or morestructures or regions in the image. The enhancement processing mayinclude a spatial domain enhancement (e.g., local mean algorithm, medianfilter algorithm, etc.), a frequency domain enhancement (e.g., low-passfiltering, high-pass filtering, etc.).

The interpolation processing may uniform the sizes of pixels or voxelsin the image. In some embodiments, the interpolation processing may beperformed on a single two-dimensional slice image so that the pixels inthe image may have a uniform size. The interpolation processing for asingle slice image may include a nearest neighbor interpolation, anatural neighbor interpolation, a bilinear interpolation, a cubicinterpolation, or the like, or any combination thereof. In someembodiments, the interpolation processing may be performed between aplurality of two-dimensional slice images to acquire cubic voxels with auniform size. The interpolation processing between slice images mayinclude an interpolation based on image gray levels, a shape-basedtarget interpolation, an image interpolation based on matching, or thelike, or a combination thereof. The interpolation based on image graylevels may include nearest neighbor interpolation, linear interpolation,spline interpolation, etc. The shape-based target interpolation mayinclude segmenting two-dimensional images, extracting ROIs and thenperforming an interpolation so that an intermediate object contour withcontinuous variations may be generated. The image interpolation based onmatching may perform image interpolation based on the matching ofcharacteristic information such as boundary contour information,structural information of an object, gray level information, structuretrend information, etc.

The morphology processing may process the shapes in image data for thepurpose of analyzing and recognizing a target by using elements havingmorphological structures (e.g., structural element with 3×3, 5×8, or anyother size or shape). Morphology processing algorithms may include adilation operation, an erosion operation, an opening operation, a closeoperation, or the like, or any combination thereof. The noise removalmay remove interference in image data or an ROI caused by machine noise,target movement, etc. Denoising algorithms may include neighborhoodaveraging, median filtering, low-pass filtering, Fourier transformation,wavelet transformation, total variation denoising, or the like, or anycombination thereof.

In 506, one or more VOIs may be determined based on the image data. Insome embodiments, the operation of determining the VOI(s) may beperformed by the VOI determination module 306. In some embodiments, theimage data may be the image data acquired in 502 or the image datapre-processed in 504.

In some embodiments, the determination of the VOI may include processingthe image data, reconstruct a three-dimensional image to implementstereo display, editing or analysis, or the like, of the target ofinterest. In some embodiments, the determination of the VOI may includethe generation of VOI contour surface (or VOI curved surface). Thegeneration of VOI contour surface may be generated using one or morecurved surface reconstruction algorithms based on boundary contourlines. The curved surface reconstruction algorithms based on boundarycontour lines may include a triangular surface reconstruction algorithm,a curved surface reconstruction algorithm for volume data, etc. Thetriangular surface reconstruction algorithm may include determining anROI contour line in a two-dimensional image, and then determine acorresponding contour line and special points (e.g., a cusp or a turningpoint where changes occur to the concave-convex degree of the curvedsurface) in an adjacent slice image, and the triangular surface may berendered based on points of boundary of the adjacent slice image,wherein the special points may be regarded as the points of boundary ofthe adjacent slice image. The triangular surface reconstructionalgorithm may include a contour connection algorithm, an opaque cubesalgorithm, a marching cubes algorithm, a dividing cubes algorithm, etc.The curved surface reconstruction algorithm for volume data mayconstruct an entity structure of the VOI using spatial units (e.g., aunit including one or more voxels), and reconstruct the VOI contoursurface by extracting non-shared faces of the spatial units. The curvedsurface reconstruction algorithm for volume data may include Delaunaytetrahedron reconstruction, a parallel hexahedron reconstruction, amarching tetrahedron reconstruction, etc.

In some embodiments, the image processing process 500 may furtherinclude displaying the VOI in a two-dimensional view and/or athree-dimensional view. In some embodiments, the image processingprocess 500 may further include determining characteristic informationof VOI voxels (e.g., gray level values, color, luminance, or otherinformation of the voxels). For instance, the surface of the VOI contoursurface and the gray level values of voxels in the contour surface maybe extracted based on the determined VOI contour surface. In someembodiments, the characteristic information of VOI voxels may bedetermined based on interpolation between two-dimensional slice images.In some embodiments, the image processing process 500 may furtherinclude optimizing the VOI based on the characteristic information ofVOI voxels. For example, during a process of segmenting a tumor, the VOImay include the tumor, and the image processing process 500 may removeblood vessels, injured tissue, calcified tissue or other tissue locatedon the surface of the tumor based on the gray level information ofvoxels in the extracted VOI.

It should be noted that the above descriptions of the image processingprocess 500 are only for the convenience of illustration, and notintended to limit the present disclosure to the scope of the exemplaryembodiments. It should be understand that for those skilled in the art,after understanding the principles of the system and method, the modulesmay be combined in any means or connected to other modules assub-systems. Various modifications and changes may be conducted on theform or details of the application fields of the system and method,without departing from the principles. For instance, operation 504 maybe omitted from the image processing process 500. Similar variationsfall within the protection scope of the present disclosure.

FIG. 6A is a flowchart of an exemplary process for determining a VOIaccording to some embodiments of the present disclosure. In someembodiments, the operation 506 of determining VOIs based on the imagedata in the process 500 may be implemented according to a process 600 asillustrated in FIG. 6A. The VOI determination process 600 may includedetermining ROIs in 601, optimizing the ROIs in 603, generating a VOI in605 and optimizing the VOI in 607.

In 601, at least two ROIs may be determined. The operation 601 may beperformed by the ROI determination unit 402. In some embodiments, the atleast two ROIs may include regions corresponding to the samethree-dimensional target of interest in different two-dimensional sliceimages. For instance, a first ROI may be determined in a firsttwo-dimensional slice image, and a second ROI may be determined in asecond two-dimensional slice image. In some embodiments, the at leasttwo ROIs may include regions of two images of interest in the samesectional plane (such as the first two-dimensional slice image and thesecond two-dimensional slice image). The sectional plane may include atraverse plane, a sagittal plane, a coronal plane or oblique plane withany angle. For example, with regard to a liver, a user may desire todetermine a tumor in the liver, and the ROI determination unit 402 maydetermine tumor regions displayed in a plurality of two-dimensionalslice images in sagittal plane.

The determination of ROI(s) may be performed automatically,semi-automatically or manually. In some embodiments, the ROIdetermination unit 402 may automatically determine an ROI according toone or more algorithms. In some embodiments, the automatic determinationof the ROI may include segmenting the ROI in a plurality oftwo-dimensional slice images based on image characteristic information.The operation of segmenting the ROI may be performed based on one ormore image segmentation algorithms. The image segmentation algorithmsmay include threshold segmentation, region growing, watershedsegmentation, morphological segmentation algorithm, statisticssegmentation, or the like, or any combination thereof. The thresholdsegmentation may be a segmentation algorithm based on regions. Forinstance, image pixels or voxels may be segmented into multiplecategories by setting different characteristic threshold. According tothe number of thresholds, the threshold segmentation may include asingle-threshold segmentation, a multi-threshold segmentation, etc.According to differences in the algorithm principles, the thresholdsegmentation may include an iterative threshold segmentation, ahistogram segmentation, a maximum between-cluster variance thresholdsegmentation, etc. The region growing algorithm may start from one ormore seed points (one seed point may be a single pixel or voxel, or maybe pixels or voxels within a certain small region), and combine adjacentpixels or voxels with characteristics (e.g., gray levels, textures,colors, etc.) similar to the seed point(s) for growing into a sameregion. Region growing may include an iterative process. For example,pixels or voxels may be added into the growing region until there may beno suitable adjacent points and the growing may be terminated. Thewatershed segmentation may be a process of iterative marking. The graylevels of pixels or voxels may be sorted in an ascending order, and thenthe pixels or voxels may be traversed in the ascending order. For eachlocal minimum gray level, a determination may be performed on at least aportion of the pixels and voxels based on a first-in, first-outstructure, and the pixels or voxels having gray levels that satisfy apre-determined condition may be marked. Then the pixels or voxels havinggray levels that satisfy the pre-determined condition may be combinedwith the point having the local minimum gray level to form an influenceregion of each local minimum gray level. A plurality of influenceregions may be further combined. The morphological segmentation may beperformed based on a Hessian point enhancing model, a Hessian lineenhancing model, a multi-scale Gaussian template matching model, amulti-scale morphological filtering model and/or an edge-basedsegmentation model. The Hessian point enhancing model may be used forenhancing, for example, a circular dot graph or a quasi-circular dotgraph. The Hessian line enhancing model may be used for enhancing alinear graph. The multi-scale Gaussian template matching model may beperformed for segmentation based on the morphology of the target to beselected. For example, in the detection of lung nodules, the multi-scaleGaussian template matching model may be performed for segmentation basedon the quasi-circular morphology of the lung nodules. The multi-scalemorphological filtering model may be performed for a filtering operationon an image by using various mathematic morphology algorithms to enhancethe target of interest. The edge-based segmentation model may include asegmentation model of a level set algorithm. The statistic model mayinclude but is not limited to a Variational Expectation Maximizationmodel, a K-means model, a Fuzzy C-means model, or the like, or anycombination thereof.

In some embodiments, the determining ROIs may further include drawingROI contour lines. In some embodiments, the ROI contour lines may bedrawn based on the segmented ROIs. In some embodiments, the drawing ofthe ROI contour lines may be performed based on one or more curvegeneration algorithms. The curve generation algorithms may include anumerical differentiation algorithm, a Bresenham algorithm, a B splinecurve generation algorithm, a Hermit curve generation algorithm, aBezier curve generation algorithm, a displacement algorithm, or thelike, or any combination thereof. In some embodiments, the generation ofROI contour lines may be performed based on a contour tracing algorithm.The contour tracing algorithm may include a reptile algorithm, a rasterscanning algorithm, a neighborhood searching algorithm, etc. The reptilealgorithm may be performed to segment a two-dimensional image into abackground region and a target region (i.e., an ROI), select a pointclose to the boundaries of the target region as an initial point6, marchby one pixel at one time, turn left in each marching after entering thetarget region from the background region, turn right after entering thebackground region from the target region, and return to the initialpoint after one circulation around the target region, resulting in atrack as the contour line of the target region. The raster scanningalgorithm may be performed to set a certain threshold and performmultiple times of row scanning and column scanning to implementtracking. The neighborhood searching algorithm may be performed togenerate a neighborhood of a solution based on a neighborhood function,and then search the neighborhood of the solution for a more superiorsolution to replace the current solution, and trace the target contourin an iterative process.

In some embodiments, in operation 601, a user may manually determineROIs in an image acquired in 502 or an image pre-processed in 504. Forinstance, a user may draw ROI contour lines manually via the interactivedevice 140 (e.g., a mouse). The user may draw one or more control pointswhen manually drawing the ROI contour lines, and the system may generatea spline curve according to the control points to obtain the ROI contourlines. The ROI determination unit 402 may determine the ROIs and/orextract the characteristic information of pixels in the ROIs based onthe ROI contour lines drawn manually. In some embodiments, a user maymanually modify or change the ROIs determined automatically by thesystem.

In some embodiments, the ROI contour lines may be closed curvesconsisting of boundaries of the same tissue. For example, during theprocess of extracting liver tissue, the ROIs may include the livertissue, and the ROI contour lines may be closed curves consisting ofboundaries of the liver tissue. In some embodiments, the ROI contourlines may be closed curves including boundaries of different tissues.For instance, during the process of segmenting a liver tumor, the ROIsmay include a portion of the liver tissue and tumor tissue, and the ROIcontour lines may include at least two segments of non-closed curves.The at least two curves may include boundary lines of the tumor tissueand boundary lines of a portion of the liver tissue.

In 603, the determined ROIs may be optimized based on characteristicinformation of the image. In some embodiments, the operation ofoptimizing the ROIs may be performed by the editing unit 410. In someembodiments, the editing unit 410 may automatically optimize the ROIsdetermined in 601 based on the characteristic information of the images.The characteristic information of the images may include gray levelinformation (e.g., grey level histogram, average grey level, the maximumor minimum grey level), texture structure, signal intensity, colorsaturation, contrast, luminance, or the like, or any combinationthereof, associated with the two-dimensional slice images where the ROIsare located. In some embodiments, the characteristic information of theimages may include the characteristic information of the pixels withinthe ROIs and/or the characteristic information of the pixels outside theROIs. In some embodiments, the editing unit 410 may automatically adjustthe region scope of the ROIs and/or re-draw the ROI contour linesaccording to the adjusted ROIs. For instance, after the ROIs aredetermined by the ROI determination unit 402 according to the level setalgorithm, the editing unit 410 may automatically distribute the pixelshaving gray levels larger than a gray level threshold in the ROIs to theoutside of the ROIs, so that the scope of the ROIs may be adjusted.

In some embodiments, the process of the optimization may includeadjusting the scope defined by the ROI contour lines. For example,during the process of extracting a tumor, the ROI contour lines may beadjusted to allow the ROIs to include the entire tumor region as much aspossible. In some embodiments, blood vessels, calcified tissue, injuredtissue, or the like included in the ROIs may be excluded from the ROIsby adjusting the scope of the ROIs. In some embodiments, the ROIs may beoptimized manually by a user. For instance, the user may drag thecontrol points on the ROI contour lines via the interactive device 140(e.g., a mouse) to adjust the scope of the ROIs. The selection of thecontrol points may be based on the characteristic information of theimages observed by the user (e.g., gray level information).

In 605, a VOI may be generated based on the ROIs. In some embodiments,the operation of generating the VOI may be performed by the VOIgeneration unit 404. The generation of the VOI may be performed based onthe at least two ROIs determined in 601, or the ROIs optimized in 603.In some embodiments, the process of generating the VOI may includegenerating a contour surface of the VOI. The contour surface of the VOImay be generated based on the ROI contour lines. The generation of VOIcontour surface based on ROI contour lines may be performed based on anyone or more curved surface reconstruction techniques described in thepresent disclosure.

In some embodiments, the VOI contour surface may be a three-dimensionalmask image. The three-dimensional mask image may refer to an image inwhich the gray level of pixels on or inside the VOI contour surface is1, and the gray level of pixels outside the VOI contour surface is 0,vice versa. Furthermore, an extraction operation of characteristicinformation (e.g., an extraction of gray level information of the VOI)may be conducted on voxels of the VOI based on the mask image. Forexample, the gray level of the mask image of the VOI contour surface maybe multiplied with the gray levels of corresponding voxels in volumedata (e.g., image data including characteristic information of voxels).As a result, the grey level information of voxels within the VOI mayremain unchanged, while the gray level information of voxels outside theVOI may be 0, and thus the gray level information of voxels in the VOImay be extracted. It should be noted that the corresponding voxels mayrefer to voxels of the target of interest displayed in volume datacorresponding to the same physical position of the voxels in the maskimage. In some embodiments, the VOI contour surface may be displayed inthe form of a three-dimensional mesh.

In 607, the generated VOI may be optimized based on characteristicinformation of the images. In some embodiments, the operation ofoptimizing the generated VOI may be performed by the editing unit 410.In some embodiments, the editing unit 410 may automatically optimize theVOI according to the characteristic information of the images. Thecharacteristic information of the images may include gray levelinformation (e.g., grey level histogram, average grey level, the maximumor minimum grey level), texture structure, signal intensity, colorsaturation, contrast, luminance, or the like, or any combinationthereof. In some embodiments, the characteristic information of theimages may include the characteristic information of the voxels withinthe VOI and/or the characteristic information of the voxels outside theVOI. In some embodiments, the process of optimization may includeadjusting the volume scope included by the VOI contour surface. Forexample, during the process of extracting a tumor, the scope of the VOIcontour surface may be adjusted to allow the VOI to include the entiretumor region as much as possible. In some embodiments, blood vessels,calcified tissue, injured tissue, or the like included in the VOI may beexcluded from the VOI by adjusting the scope of the VOI. In someembodiments, the optimization of the VOI may be performed based onmanual optimization by a user, or may be implemented by automaticoptimization. For instance, the user may drag the point(s) on the VOIcontour surface via the interactive device 140 (e.g., a mouse) to adjustthe scope of the VOI. As another example, a gray level threshold may beset based on the gray level information of voxels in the VOI, and theediting unit 410 may automatically distribute the voxels having graylevels larger than the gray level threshold in the VOI to the outside ofthe VOI, so that the scope of the VOI may be adjusted.

It should be noted that the above descriptions of the process 600 ofdetermining a VOI are only for the convenience of illustration, and notintended to limit the present disclosure to the scope of the exemplaryembodiments. It should be understand that for those skilled in the art,after understanding the principles of the system and method,modifications may be conducted on the process 600 of determining a VOI,without departing from the principles. In some embodiments, operations603 and/or 607 may be omitted from the process 600 of determining theVOI. In some embodiments, operations 601 and 603 may be combined intoone operation. For instance, the ROI determination unit 402 maydetermine an ROI based on the characteristic information of the image(s)without an extra optimization operation for the ROIs. In someembodiments, operations 605 and 607 may be combined into one operation.For instance, the VOI generation unit 404 may generate a VOI based onthe characteristic information of the image(s) without an extraoptimization operation for the VOI. Similar variations fall within theprotection scope of the present disclosure.

FIG. 6B is a flowchart of an exemplary process for determining a VOIand/or an ROI according to some embodiments of the present disclosure.In some embodiments, the operation 506 of determining a VOI based onimage data in the process 500 may be implemented according to theprocess 650 as illustrated in FIG. 6B.

In 651, a volume rendering (VR) image may be reconstructed based onimage data. In some embodiments, operation 651 may be performed by thepre-processing module 304. In some embodiments, the image data may bethe image data acquired in operation 502, including two-dimensionalslice image data and/or three-dimensional slice image data. In 651,volume rendering techniques (VRT) may be used to composite a pluralityof two-dimensional images to a three-dimensional stereo image, and setthe CT values of the voxels of the three-dimensional stereo image asdifferent transparencies that varies from completely opaque tocompletely transparent. Meanwhile, the three-dimensional stereo imagemay be displayed with different gray scales (or pseudo colors) using avirtual illumination effect. For example, in the reconstruction processof a VR image of head and neck, the transparency of skull tissue isrelatively low, while the transparency of blood vessel tissue isrelatively high, and thus the gray scales (or pseudo colors) of theskull and blood vessels displayed in the VR image may be different. Insome embodiments, in the reconstruction of the VR image, a virtualprojection ray may pass the image data by a pre-determined angle, thepixels or voxels in different slice images of the image data (e.g.,multiple two-dimensional slice images) on the projection line may beprojected, and the information of pixels or voxels in differenttwo-dimensional slice images may be comprehensively displayed.

In 653, a VOI may be segmented based on a region growing algorithm. Insome embodiments, the operation of VOI region growing may be performedby the VOI generation unit 404. In some embodiments, the VOI regiongrowing may be performed based on the VR image. For instance, the regiongrowing algorithm may include selecting at least one seed point as theinitial point for growing based on the VR image; combining neighborhoodvoxels around the seed point to the region where the seed point islocated, wherein the voxels to be combined have the same characteristicsas or similar characteristics to the seed point; determining the growingvoxels as seed points and continue growing around until no voxels thatsatisfy a predetermined condition may be found and then the growingprocess may be terminated. In some embodiments, the seed point mayinclude one voxel or a region including a plurality of voxels. In someembodiments, the similar characteristics of the voxels or pixels mayinclude texture, gray level, average gray level, signal intensity, colorsaturation, contrast, luminance, or the like, or any combinationthereof. In some embodiments, in the process of region growing, acontinuous dynamic process of the VOI growing may be observed based onthe changes of newly added seed points. For example, for the regiongrowing of blood vessel tissue, a user may perform the growing of bloodvessel tissue based on a VR image, and observe the changing process ofthe growing of the blood vessel tissue, such as whether the growing isoverflowed/incomplete or not.

In some embodiments, the region growing of the VOI may be performedbased on image data. This may indicate that operation 651 may beperformed after 653. For instance, at least one seed point in atwo-dimensional slice image may be selected as the initial point forgrowing; neighborhood voxels around the seed point may be combined inthe region where the seed point is located, wherein the voxels to becombined have the same characteristics as or similar characteristics tothe seed point; the growing voxels may be determined as new seed points,and growing process may proceed until no voxels that satisfy apredetermined condition may be found and then the growing process may beterminated; and then a VR image of the VOI may be reconstructed based onthe VOI obtained through region growing and the volume renderingtechnique.

In 655, user interaction information may be obtained. In someembodiments, the operation 655 may be performed by the interactivedevice 140. The interactive information may include rotating an image,scaling an image, translating an image, suspending region growing, stopregion growing, or the like, or any combination thereof. In someembodiments, the user interactive information may include stop regiongrowing after suspending region growing. In some embodiments, a user mayperform the interactive operations by the interactive device 140 (e.g.,a mouse).

In 657, the VOI may be updated based on the user interactiveinformation. In some embodiments, the operation of 657 may be performedby the VOI generation unit 404 and/or the updating unit 412. The userinteractive information may suspend the process of the region growing.In some embodiments, in 657, at least a portion of the VOI may bedisplayed. For instance, in a VR image, there may be a maskingrelationship between the growing VOI (e.g., blood vessels) and othertissue (e.g., skeletons). During the process of region growing, a usermay observe the entire VOI formed by region growing, but may not be ableto observe the portion of VOI covered by other tissue. The portion ofVOI that is not covered by other tissue may be displayed when the regiongrowing is suspended, and thus it may be convenient for a user toobserve the masking relationship between the VOI and other tissue. Forexample, during the growing process of blood vessel tissue of head andneck, newly growing blood vessels may be displayed through a VR image sothat it may be convenient for the user to observe the changes in thegrowing process. In the process of region growing, in order to observethe masking relationship between the currently growing blood vessels andthe skull, a user may rotate the region of growing and observe themasking relationship of the growing blood vessels and the skull. In someembodiments, the speed of region growing may be controlled based on theuser interaction information. In some embodiments, the speed of regiongrowing may be controlled based on the extraction frequency of seedpoints. For instance, for a relatively small VOI, the time required forregion growing may be relatively short. For the relatively small VOI,the extraction frequency of seed points during region growing may berelatively large, and the speed of region growing may be relativelyhigh. However, a user may desire to have enough time to observe thegrowing process of the relatively small VOI, and thus the speed ofregion growing may be decreased by decreasing the extraction frequencyof seed points. As another example, for a relatively large VOI, the timerequired for region growing may be relatively long. For the relativelylarge VOI, the extraction frequency of seed points during the earlystage of region growing may be relatively small. However, as the seedpoints increase, the number of seed points extracted in each growing mayincrease fast. In the late stage of region growing, the growing may beexcessively fast and may easily overflow. Thus the user may desire thatthe region growing process for the relatively large VOI may be completedfast and the speed of region growing in the late stage may be stable.Therefore, in the early stage of region growing, the extractionfrequency of seed points may be increased so as to increase the speed ofregion growing in the early stage, while in the late stage of regiongrowing, the extraction frequency of seed points may be decreased so asto decrease the speed of region growing and cause the speed of regiongrowing to tend to be stable.

In 659, one or more ROIs may be determined based on the VOI. In someembodiments, the operation 659 may be performed by the ROI determinationunit 402. In some embodiments, the VOI may be the dynamically changingVOI during the process of region growing, or may be the generatedportion of a VOI when the region growing is suspended. In someembodiments, in 659, slice processing may be performed on the VOI toobtain two-dimensional slice images. The VOI may be marked in thetwo-dimensional slice images and displayed as ROIs. In some embodiments,in 659, the ROIs may be displayed synchronously in an MPR window whenthe VOI is displayed in a VR window.

It should be noted that the above descriptions of the process 650 areonly for the convenience of illustration, and not intended to limit thepresent disclosure to the scope of the exemplary embodiments. It shouldbe understand that for those skilled in the art, after understanding theprinciples of the present disclosure, various modifications may beconducted without departing from the principles. For instance,operations 655, 657 and/or 659 may be omitted from the process 650. Insome embodiments, the process 650 may include pre-processing the imagedata. Similar variations fall within the protection of the presentdisclosure.

FIG. 7 is a flowchart of an exemplary process for determining a VOIaccording to some embodiments of the present disclosure.

In 702, image data may be acquired. Operation 702 may be performed bythe image data acquisition module 302. In some embodiments, the imagedata may be traverse plane image data, coronal plane image data,sagittal plane image data or oblique plane image data. The image datamay include at least two slices of the two-dimensional slice images. Insome embodiments, more descriptions of the acquisition of the image datamay be found elsewhere in the present disclosure (e.g., FIG. 5 anddescriptions thereof).

In 704, the image data may be pre-processed. Operation 704 may beperformed by the pre-processing module 304. In some embodiments, thepre-processing may include a pre-processing operation performed on anyslice of the two-dimensional slice images. In some embodiments, thepre-processing may include a pre-processing operation (e.g.,interpolation processing) performed on the at least two slices of thetwo-dimensional slice images. In some embodiments, more descriptions ofthe pre-processing of the image data may be found elsewhere in thepresent disclosure (e.g., FIG. 5 and descriptions thereof).

In 706, at least two ROIs may be extracted based on the image data.Operation 706 may be performed by the ROI determination unit 402. Theimage data may be the image data acquired in 702 or the image datapre-processed in 704. In some embodiments, the at least two ROIs may belocated in different slices of the two-dimensional slice images. In someembodiments, 706 may extract the characteristic information of the atleast two ROIs (i.e., ROI segmentation) and/or extract the at least twoROIs. In some embodiments, contour lines of the at least two ROIs may bedrawn according to any curve generation algorithm described in thepresent disclosure.

In 708, at least one ROI may be edited. Operation 708 may be performedby the editing unit 410. In some embodiments, the editing at least oneROI may include adjusting the scope of the ROI based on characteristicinformation of the image(s). In some embodiments, at least two controlpoints may be extracted based on one ROI extracted in 706. The controlpoints may be key points or high curvature points on an ROI contour linethat determine the shape of the contour line. The curve generation unit406 may generate a spline curve by an interpolation operation on thecontrol points. Then the editing unit 410 may edit the control pointsand cause the spline curve generated based on the control points tocoincide with the ROI contour line as much as possible. In someembodiments, the ROI may be redetermined based on a spline curvegenerated based on the edited control points. In some embodiments, theextraction algorithms of the control points may include a polygonapproximation algorithm based on splitting and mergence, a polygonapproximation algorithm based on area error, etc. The polygonapproximation algorithm based on splitting and mergence may start from apolygon corresponding to an original contour line of the ROI andrepetitively perform an iteration process of splitting and mergence on asegmental arc that is to be segmented. The process may start at a pointon an original segmental arc that may serve as a division point. Theoriginal segmental arc may be segmented into two segmental arcs, and thedivision point may become a new vertex of an approximate polygon.Meanwhile, one side of the original approximate polygon may be removedand a new side may be added. The operation of splitting and mergence maybe repetitively performed on the upper portion and the bottom portion ofthe segmental arcs, and thus characteristic points on the boundary linesof the top border and characteristic points on the boundary lines of thebottom border may be obtained. The polygon approximation algorithm basedon area error may use the influence extent of each pixel of the originalcontour line on the regional area included by the entire originalcontour line as weight, gradually remove the pixels/voxels having arelatively small influence on the change of the area, keep thepixels/voxels having a relatively apparent influence on the change ofthe area, and finally cause the area error to satisfy a certain errorthreshold. The pixels/voxels having a relatively apparent influence onthe change of the area may serve as control points. In some embodiments,the editing unit 410 may determine whether a difference between theregional area included by the original contour line and the regionalarea included by the contour line re-generated after the removal of apixel/voxel is less than a pre-determined value. If the differenceassociated with the regional area is less than the pre-determined value,the pixel/voxel may be determined as a point having a relatively smallinfluence on the change of the area.

In 710, a VOI may be generated based on the at least two ROIs. Operation710 may be performed by the VOI generation unit 404. In someembodiments, the at least two ROIs may be the ROIs determined in 706 orthe ROIs edited in 708. In some embodiments, the VOI may be generatedaccording to any volume rendering algorithm described in the presentdisclosure. For instance, characteristic information of voxels in theVOI may be generated by performing an interpolation operation on thecharacteristic information of pixels in the at least two ROIs, and theVOI may be extracted based on the generated characteristic informationof voxels in the VOI. In some embodiments, the VOI may be generatedaccording to any surface rendering algorithm described in the presentdisclosure. For example, contour surface(s) of the VOI may be generatedby performing an interpolation operation on the contour lines of the atleast two ROIs, and the VOI may be extracted based on the contoursurface(s) of the VOI.

In 712, the VOI may be edited. Operation 712 may be performed by theediting unit 410. In some embodiments, the scope of the VOI may beadjusted based on image characteristic information (e.g., gray levels ofpixels or voxels) in 712. For instance, through setting a gray levelthreshold, the voxels that have larger gray level values than the graylevel threshold may be determined to belong the outside of the scope ofthe VOI. In some embodiments, one or more ROIs in the VOI may be edited.For example, the scope of the ROIs may be adjusted by editing thecontrol points of the ROIs, and then the VOI may be re-generated basedon the edited ROIs.

In 714, the at least two ROIs and the VOI may be displayedsynchronously. Operation 714 may be performed by the display module 310.In some embodiments, images in different sectional planes (e.g., imagesin the traverse plane, the coronal plane and the sagittal plane) of thesame ROI may be displayed simultaneously in the same two-dimensionaland/or three-dimensional display window in 714. In some embodiments, ROIimages in different sectional planes corresponding to the same VOI(e.g., ROI images in the traverse plane, the coronal plane and thesagittal plane corresponding to the same VOI) may be displayedsimultaneously in the same two-dimensional and/or three-dimensionaldisplay window in 714. In some embodiments, the two dimensional displaywindow may be an MPR view window. The three-dimensional display windowmay be a volume rendering view window. In some embodiments, in 714, theVOI and the ROI(s) may be displayed simultaneously in thethree-dimensional window. In 714, the sectional planes where the ROI(s)are located may be determined in the VOI, and the ROI(s) may be markedand/or displayed. In some embodiments, the display may be performed inreal time. In some embodiments, 714 may be performed simultaneously withany of 706, 708, 710, 712, etc. For example, if the operation 706 isbeing performed (e.g., if a user is rendering two-dimensional ROI(s)),the display module 310 may position the drawn ROI contour line(s) in thethree-dimensional VOI in real time, and/or mark (and/or display) theROI(s) in the three-dimensional VOI. If the number of the rendered ROIslices reaches two, the display module 310 may automatically display thethree-dimensional VOI generated according to the currently renderedtwo-dimensional ROI(s) in the three-dimensional display window. In someembodiments, the display may not be performed in real time. For example,after a user has completed the rendering of a plurality of ROIs in aplurality of two-dimensional slice images, the display module 310 maydisplay the VOI generated based on the plurality of ROIs in thethree-dimensional display window.

In 716, whether the ROI(s) or the VOI satisfy a pre-determined conditionmay be judged. Operation 716 may be performed by the judgment unit 414.In some embodiments, the pre-determined condition may relate to a factthat the ROIs or the VOI do not include at least a portion of bloodvessels, calcified tissue or injured tissue, etc. If the ROIs or the VOIsatisfy the pre-determined condition (e.g., the ROIs or the VOI do notinclude at least a portion of blood vessels, calcified tissue or injuredtissue), then operation 718 may be perform; if the ROIs or the VOI donot satisfy the pre-determined condition, then the process 700 mayreturn to 708 and continue to edit at least one ROI. In someembodiments, the judgment unit 414 may judge whether the ROIs or the VOIsatisfy the pre-determined condition based on characteristic informationof the ROIs or the VOI. For instance, the judgment unit 414 may judgewhether the gray level values of all the pixels or voxels in the ROIs orthe VOI are less than a pre-determined gray level threshold. If the graylevel values of all the pixels or voxels are less than thepre-determined gray level threshold, then the ROIs or the VOI may notsatisfy the pre-determined condition; if the gray level values of atleast a portion of the pixels or voxels are larger than thepre-determined gray level threshold, then the ROIs or the VOI maysatisfy the pre-determined condition.

In 718, the VOI may be determined. Operation 718 may be performed by theVOI determination module 306. In some embodiments, in 718, thecharacteristic information of voxels in the VOI may be extracted, thecharacteristic information of pixels in the ROIs may be extracted,and/or the contour surface(s) of the VOI may be determined.

It should be noted that the above descriptions of the process 700 fordetermining a VOI are only for the convenience of illustration, and notintended to limit the present disclosure to the scope of the exemplaryembodiments. It should be understand that for those skilled in the art,after understanding the principles of the present disclosure, variousmodifications may be conducted to the process 700 for determining a VOIwithout departing from the principles. For example, operations 704and/or 708 may be omitted from the process 700 for determining a VOI.Similar variations fall within the protection scope of the presentdisclosure.

FIG. 8 is a flowchart of an exemplary process for generating a curvedsurface according to some embodiments of the present disclosure. In someembodiments, the operation 710 of generating a VOI based on ROIs in theprocess 700 may be according to the process 800 as illustrated in FIG.8. In operation 802, a type of a curved surface of interest to begenerated may be determined. Operation 802 may be implemented by thecurved surface generation unit 408. In some embodiments, the curvedsurface of interest may be a closed curved surface. The closed curvedsurface may be generated based on a two-dimensional slice image in atraverse plane, a sagittal plane, a coronal plane or an opaque plane ofany angle. In some embodiments, the curved surface of interest may be anon-closed curved surface. The non-closed curved surface may segment thetarget of interest into at least two portions. In some embodiments, thetype of the curved surface of interest may be determined in 802according to a division scheme. The division scheme may include divingthe target of interest into different portions such as top and bottomportions, left and right portions, front and back portions or any otherorientations. The top and bottom portions, left and right portions,front and back portions or other orientations may be determined based onthe direction of the front view of the target of interest. For instance,a portion close to the head of an object may be the top portion; aportion close to the feet of the object may be the bottom portion; aportion close to the left of the object may be the left portion; aportion close to the right of the object may be the right portion; aportion close to the front chest of the object may be the front portion,a portion close to the back of the object may be the back portion. Ifthe target of interest is segmented into top and bottom portions, thecurved surface of interest may use the direction in a traverse plane asa reference, and the generation of the curved surface of interest may beperformed based on two-dimensional image(s) in a sagittal plane or acoronal plane. If the target of interest is segmented into left andright portions or front and back portions, the curved surface ofinterest may use the direction in a sagittal plane or a coronal plane asa reference, and the generation of the curved surface of interest may beperformed based on two-dimensional image(s) in a traverse plane.

In 804, image data may be acquired. The image data may include Ntwo-dimensional slice images, wherein N may be an integer larger than 0.Operation 804 may be implemented by the image data acquisition module302. In some embodiments, at least one of the N slice images may includethe target of interest. A displayed region of the target of interest ina certain two-dimensional sectional slice image may be considered as anROI. In some embodiments, the acquisition of the image data may beperformed based on the type of the curved surface determined in 802. Forexample, if the curved surface segments the target of interest into topand bottom portions, then the image may be a two-dimensional slice imagein a sagittal plane or a coronal plane. If the curved surface segmentsthe target of interest into front and back portions or left and rightportions, then the image may be a two-dimensional slice image in atraverse plane.

In 806, one or more control points may be determined based on an i^(th)two dimensional slice image, wherein i may be a positive integer lessthan or equal to N (i.e., 0≤i≤N). Operation 806 may be implemented bythe curve generation unit 406. In some embodiments, the i^(th) twodimensional slice image may be pre-processed in 806. In 806, an initialsegmentation of an ROI may be performed on the two-dimensional sliceimage or the pre-processed two-dimensional slice image to obtain anoriginal ROI. The original ROI may include at least one portion of thetarget of interest. The initial segmentation may be performed based onone or more segmentation algorithms described in the present disclosure.In 806, an original ROI contour line may be drawn manually orautomatically based on the boundary of the original ROI, and/or thecontrol points may be determined based on the gray level information ofpixels in the original ROI. In some embodiments, the control points mayinclude characteristic points located on or around the original ROIcontour line (or the boundary) or characteristic points located withinthe original ROI. The characteristic points may be key points or highcurvature points located on the contour line (or the boundary)representing the original ROI or the division line(s) of differentregions in the original ROI, and the key points or high curvature pointsmay determine the shape of the contour line (or the division line).

In some embodiments, extraction algorithms of the characteristic pointsmay be implemented using point probe operator(s), for example, atemplate matching algorithm, a geometrical characteristic detectionalgorithm, etc. The template matching algorithm may set a series oftemplates for characteristic points (e.g., angular points, cross points,etc.) and judge whether the pixel(s) located in the center ofsub-windows are characteristic points according to the similarity of thetemplates and all the image sub-windows. The geometrical characteristicdetection algorithm may include an extraction algorithm based onboundary curvatures, an extraction algorithm based on gray levelinformation of an image, etc. In some embodiments, the point probeoperators may include Harris operator, Forstner operator, Susanoperator, MIC operator, Moravec operator, SIFT operator, or the like, orany combination thereof.

In 808, a spline curve may be determined based on the control points.Operation 808 may be performed by the curve generation unit 406. In someembodiments, the control points may be processed based on aninterpolation algorithm to generate the spline curve. The interpolationalgorithm may include a smooth interpolation with unequal intervals, anearest-neighbor interpolation algorithm, a bilinear interpolationalgorithm, a bicubic gray-level interpolation algorithm, a space-variantlinear gray-level interpolation algorithm, a fractal interpolationalgorithm, or the like, or any combination thereof.

In some embodiments, the spline curve may be a closed curve. The closedcurve may include at least a portion of the original ROI. For instance,if a liver image is processed, the spline curve may be a boundary lineof liver tissue to distinguish liver tissue and non-liver tissue. Insome embodiments, the spline curve may be a non-closed curve (such asthe spline curve SE as illustrated in FIG. 10A or 10B). The non-closedcurve may segment the original ROI into at least two portions. Further,the non-closed curve and a portion of the contour line of the originalROI may form a closed curve, and the closed curve may include a targetof interest. As illustrated in FIG. 10A, the closed curve formed by thespline curve SE and the bottom boundary of the region B may determine acertain segment of the liver (as illustrated by the region B).

In 810, a target ROI may be determined based on the spline curve.Operation 810 may be performed by the ROI determination unit 402. Insome embodiments, the target ROI may include at least a portion of theoriginal ROI. In some embodiments, the scope of the target ROI may beadjusted based on the control points on the spline curve. For example, auser may adjust the spline curve by manually dragging the controlpoint(s) to other position(s) so as to adjust the scope of the targetROI. In some embodiments, the target ROI may also be adjusted based onimage characteristic information. For instance, a gray level thresholdmay be set and used to remove pixel(s) in the target ROI that havelarger or smaller gray level values than the gray level threshold.

In 812, whether the (i+1)^(th) two-dimensional slice image includes aregion of interest may be judged. Operation 812 may be performed by thejudgment unit 414. The ROI may refer to the displayed region of thetarget of interest in the (i+1)^(th) two-dimensional slice image. If the(i+1)^(th) two-dimensional slice image includes the ROI, then operation814 may be performed, and operation 806 may be performed based on the(i+1)^(th) two-dimensional slice image. If the (i+1)^(th)two-dimensional slice image does not include the ROI, then operation 816may be performed. In some embodiments, the judgment may be performed bya user through observing the image displayed in a two-dimensionaldisplay window in 812. In some embodiments, the judgment may beautomatically performed according to image characteristic information.For instance, whether the (i+1)^(th) two-dimensional slice imageincludes the ROI may be judged by comparing the gray level informationof the (i+1)^(th) two-dimensional slice image and the i^(th)two-dimensional slice image.

In 816, the curved surface of interest may be generated based on thespline curve. Operation 816 may be performed by the curved surfacegeneration unit 408. In some embodiments, if the spline curve is aclosed curve, then the curved surface of interest may be a closed curvedsurface. The closed curved surface may be a VOI contour surface. In someembodiments, if the spline curve is a non-closed curve, then the curvedsurface of interest may be a non-closed curved surface. In someembodiments, the non-closed curved surface may segment the VOI into atleast two portions. In some embodiments, the curved surface of interestmay be a two-dimensional or three-dimensional mask image. In someembodiments, the curved surface of interest may be displayed in the formof a mesh. In some embodiments, in 816, the spline curve and the controlpoints may be displayed in a two-dimensional display window, the curvedsurface of interest may be displayed synchronously in athree-dimensional display window, and/or the spline curve and thecontrol points may be marked (and/or displayed) in the curved surface ofinterest.

In 818, a target VOI may be generated based on the curved surface ofinterest. Operation 818 may be performed by the VOI generation unit 404.In some embodiments, if the curved surface of interest is a closedcurved surface, then the characteristic information of voxels in thetarget VOI may be extracted according to the closed curved surface in818. In some embodiments, if the curved surface of interest is anon-closed curved surface, then in 818, an original VOI generated by aninitial segmentation may be segmented into at least two portionsaccording to the non-closed curved surface. The original VOI may beobtained by an initial segmentation of image data performed in any ofoperations 808 through 818. The non-closed curved surface and a portionof the original VOI contour surface may form a closed curved surface,i.e., the contour surface of the target VOI. In some embodiments, thetarget VOI may be certain tissue or an organ or a portion thereof, forexample, a liver, a certain segment of tissue in a liver or a tumor in aliver, etc.

It should be noted that the above descriptions of the process 800 forgenerating a curved surface are only for the convenience ofillustration, and not intended to limit the present disclosure to thescope of the exemplary embodiments. It should be understand that forthose skilled in the art, after understanding the principles of thepresent disclosure, various modifications may be conducted to theprocess 800 for generating a curved surface without departing from theprinciples. The foregoing or following operations may not necessarily beperformed according to the describe order. On the contrary, variousoperations may be performed simultaneously or in a reverse order.Meanwhile, other operations may be added into the process or one or moreoperations of operations may be removed from the process. For example,operation 804 may be performed first, then operation 802 may beperformed, or operations 802 and 804 may be performed simultaneously. Asanother example, the process 800 for generating a curved surface mayfurther include pre-processing the acquired image data. Similarvariations fall within the protection scope of the present disclosure.

FIG. 9 is a flowchart of an exemplary process for generating and editinga curved surface in a multiplanar reconstruction window and/or a volumerendering window according to some embodiments of the presentdisclosure. In some embodiments, the operation 708 of editing at leastone ROI and/or the operation 710 of generating a VOI based on the ROI(s)in the process 700 may be implemented according to the process 900 asillustrated in FIG. 9.

In 902, a type of a curved surface may be determined. Operation 902 maybe performed by the curved surface generation unit 408. In someembodiments, for different types of curved surface, spline curves may bedrawn in image view windows of different sectional planes. Thedetermination of the type of a curved surface may be performed based onthe function of the curved surface. For example, if the curved surfaceis used to segment the target of interest into front and back portionsor left and right portions, the spline curve(s) may need to be drawn inan MPR window of a traverse plane. As another example, if the curvedsurface is used to segment the target of interest into upper and bottomportions, then the spline curve(s) may need to be drawn in an MPR windowof a coronal plane or a sagittal plane. More descriptions of thedetermination of the type of the curved surface may be found elsewherein the present disclosure (e.g., FIG. 8 and descriptions thereof).

In 904, one or more spline curves may be determined. Operation 904 maybe performed by the curve generation unit 406. The spline curve(s) maybe drawn based on two-dimensional slice image(s) displayed in an MPRwindow of one or more sectional planes. In some embodiments, in 904, atleast one control point may be determined based on the target ofinterest in a two-dimensional slice image, and a spline curve may begenerated based on the at least one control point. Similarly, aplurality of spline curves may be determined in a plurality oftwo-dimensional slice images in 904. In some embodiments, the splinecurves may be contour lines of the target of interest. In someembodiments, the spline curve may be a segmentation line for differentregions in the target of interest, and the segmentation line may segmentthe target of interest into at least two portions. In some embodiments,a list of spline curves including a plurality of spline curves in aplurality of two-dimensional slice images may be generated. The list ofspline curves may include at least two spline curves. More descriptionsof the selection of control points and the determination of the splinecurves may be found elsewhere in the present disclosure (e.g., FIG. 8and the descriptions thereof).

In 906, whether to generate a curved surface mesh may be judged.Operation 906 may be performed by the judgment unit 414. In someembodiments, whether to generate a curved surface mesh may be judgedaccording to user requirement(s). For instance, a user may set a numberthreshold for the spline curves, and the judgment unit 414 may judgewhether the number of spline curves determined in 904 is larger than thethreshold. If the number of the spline curves is larger than thethreshold, then in 906, the judgment result may be that a curved surfacemesh may be generated. In some embodiments, in 906, whether to generatea curved surface mesh may be judged based on whether the currenttwo-dimensional slice image includes the target of interest. In someembodiments, a user may observe whether the current two-dimensionalimage includes the target of interest in an MPR window. The user mayinput instruction(s) via the interactive device 140 (e.g., a keyboard, amouse, etc.) based on the observation result, and the judgment unit 414may judge whether to generate a curved surface mesh based on theinputted instructions. In some embodiments, whether the currenttwo-dimensional slice image includes the target of interest may beautomatically judged based on gray level information of the image. Forinstance, in 906, the gray level information of the target of interestmay be compared with the gray level information of the currenttwo-dimensional slice image to judge whether the current two-dimensionalslice image includes the target of interest. If it is determined togenerate the curved surface mesh, then operation 910 may be performed.If it is determined not to generate the curved surface mesh, thenoperation 908 may be performed. In 908, one or more pages may be turnedin an MPR window to obtain a next two-dimensional slice image. After theoperation 908, the process 900 may return to 904 and continue todetermine a spline curve.

In 910, the curved surface may be generated based on the spline curve.Operation 910 may be performed by the curved surface generation unit408. In some embodiments, the generation of the curved surface may beperformed using any curved surface reconstruction technique described inthe present disclosure. In some embodiments, the curved surface may be amask image. In some embodiments, the curved surface may be displayed inthe form of a mesh. In some embodiments, the spline curve may bedisplayed in an MPR window, and the generated curved surface may bedisplayed simultaneously in a volume rendering window. In someembodiments, at least one spline curve and/or the control points on thespline curve may be marked on the curved surface and displayed.

In 912, an editing type may be determined. Operation 912 may beperformed by the editing unit 410. The editing type may refer to thetype of a display window. The editing type may include editing thespline curve in an MPR window or editing the spline curve in a volumerendering window (i.e., the curved surface mesh). The editing type maybe selected by a user, or the system may automatically select a defaultediting type. If an MPR window is selected for editing the spline curve,then operation 914 may be performed; if a volume rendering window isselected for editing the spline curve, then operation 918 may beperformed.

In 914, whether the current two-dimensional slice image displayed in theMPR window includes control points may be determined. Operation 914 maybe performed by the judgment unit 414. If the current two-dimensionalslice image does not include control points, then 916 may be performedto turn one or more pages in the MPR window, and the judgment may beperformed on the next two-dimensional slice image. If the currenttwo-dimensional slice image includes control points, then operation 918may be performed.

In 918, the control point(s) may be edited. Operation 918 may beperformed by the editing unit 410. The control points may be edited inthe MPR window and/or the volume rendering window or the mesh renderingwindow. In some embodiments, a user may implement the editing of thecontrol point(s) by dragging the control point(s) via the interactivedevice 140 (e.g., a mouse). In some embodiments, if the control point(s)are edited in the MPR window, then the spline curve displayed in the MPRwindow and/or the curved surface displayed in the volume renderingwindow may be updated synchronously according to the edited controlpoint(s). If the control points are edited in the volume renderingwindow or mesh rendering window, then the curved surface displayed inthe volume rendering window may be updated synchronously according tothe edited control point(s), and the spline curve where the controlpoints are located may be updated synchronously in the MPR window. Forinstance, in the volume rendering window or the mesh rendering window, athree-dimensional curved surface generated based on the spline curve(s)drawn in the MPR window may be displayed in real time, and the volumerendering window or the mesh rendering window may synchronously displaythe spline curve(s) and the control point(s) that form the splinecurve(s). The three-dimensional curved surface or the spline curve maybe adjusted by adjusting the control point(s) in the three-dimensionalcurved surface.

In 920, whether to continue editing may be determined. Operation 920 maybe performed by the judgment unit 414. If it is determined to continueto edit the control point(s), then the process 900 may return to 912,and relevant operation(s) may be repeated. Otherwise, operation 922 maybe performed. In some embodiments, whether to continue to edit thecontrol point(s) may be determined based on whether the curved surfacedisplayed in the volume rendering window satisfies user requirement(s)or one or more pre-determined conditions. The pre-determinedcondition(s) may include whether the curved surface includes at least aportion of blood vessels, calcified tissue, injured tissue, etc. In someembodiments, the judgment unit 414 may determine whether the curvedsurface satisfies the pre-determined condition(s) based oncharacteristic information of the curved surface. For example, thejudgment unit 414 may determine whether the gray level of all the pixelsor voxels in the curved surface is less than a pre-determined gray levelthreshold. If the gray level of all the pixels or voxels is less thanthe pre-determined threshold, then the curved surface may not satisfythe pre-determined condition. If the gray level of at least a portion ofthe pixels or voxels is greater than the pre-determined gray levelthreshold, then the curved surface may satisfy the pre-determinedcondition.

In 922, the cured surface may be generated based on the edited splinecurve. Operation 922 may be performed by the curved surface generationunit 408. In some embodiments, the curved surface may be generated basedon an interpolation algorithm in 922.

It should be noted that the above descriptions of the process 900 forgenerating a curved surface are only for the convenience ofillustration, and not intended to limit the present disclosure to thescope of the exemplary embodiments. It should be understand that forthose skilled in the art, after understanding the principles of thepresent disclosure, various modifications may be conducted to theprocess 900 for generating a curved surface without departing from theprinciples. For example, generating the curved surface and/or editingoperation may include acquiring image data. As another example,generating the curved surface and/or editing operation may furtherinclude pre-processing the image data. Similar variations fall withinthe protection scope of the present disclosure.

FIGS. 10A and 10B are schematic diagrams of an exemplary spline curveaccording to some embodiments of the present disclosure. FIG. 10A is atwo-dimensional slice image of liver tissue in a traverse plane. Thespline curve SE may segment the liver region into A and B portions inthe slice image in the traverse plane. A segmentation curved surface forthe liver may be generated based on spline curves in a two-dimensionalslice image sequence in the traverse plane, wherein the curved surfacemay segment the liver tissue into front and back portions. FIG. 10B is atwo-dimensional slice image of liver tissue in a sagittal plane. Thespline curve SE segments the liver region into C and D portions in thetwo-dimensional slice image in the sagittal plane. The segmentationcurved surface for the liver may be generated based on the spline curvesin a plurality of two-dimensional slice images in the sagittal plane,wherein the curved surface may segment the liver tissue into top andbottom portions.

FIG. 11 is a flowchart of an exemplary process for performing regiongrowing on a VOI based on volume rendering (VR) according to someembodiments of the present disclosure. In some embodiments, theoperation 653 of segmenting a VOI based on a region growing algorithm inthe process 650 may be implemented according to the process 1100illustrated in FIG. 11. In 1102, at least one initial seed point may bedetermined. In some embodiments, operation 1102 may be performed by theVOI generation unit 404. In some embodiments, the initial seed point mayinclude at least one voxel. In some embodiments, the initial seed pointmay be a region, and the region may include a set of a plurality ofvoxels. In some embodiments, the selection of the initial seed point maybe performed based on a VR image or image data (e.g., a two-dimensionalslice image). In some embodiments, the initial seed point may bedetermined manually. For example, a user may select the initial seedpoint via the interactive device 140 (e.g., a mouse). In someembodiments, the initial seed point may be selected automatically. Forexample, the initial seed point may be the voxels or pixels having thelargest probability of appearance in the gray level histogram of theimage. As another example, the initial seed point may be the centerpoint of the image. In some embodiments, the image may be segmentedbased on one or more of the image segmentation algorithms described inthe present disclosure, and then the seed point may be selected based onthe segmented image. For instance, the initial seed point may bedetermined based on a watershed segmentation algorithm. In someembodiments, the image may be segmented into a plurality of rectangularregions, and the center point of each rectangular region may be used asan initial seed point. For example, an edge detection may be performedon the image, and the center point of an adjacent boundary region, anypoint in a closed region detected by the edge detection, or a localminimum value point may be selected as the initial seed point. Asanother example, a gray level gradient image may be obtained using amathematical morphology algorithm, and then the initial seed point maybe selected in the regions with relatively small and/or large change(s)in the gray level gradient image.

In some embodiments, the initial seed point may be selected based on oneor more selection standards in operation 1102. The selection standard(s)may include the similarity of the characteristics of the initial seedpoint and the characteristics of adjacent pixels or voxels. Thecharacteristics may include gray level values, colors, texture,luminance, or the like, or any combination thereof. The selectionstandard(s) may include a similarity function, a spectral angel, aspectral distance, a normalizing vector distance, etc. The similarityfunction may be used to measure the similarity of the characteristics oftwo voxels, such as a similarity coefficient value function, a distancefunction, etc. The similarity coefficient value function may be used tomeasure the similarity of two voxels based on a similarity coefficientvalue. The similarity coefficient value may be relatively large if thetwo voxels are similar; the similarity coefficient value may berelatively small if the two voxels are less similar. In a distancefunction, each voxel may be considered as a point in a high-dimensional(e.g., four-dimensional or higher) space, and then a certain distance(e.g., Mahalanobis distance, Euclidean distance, etc.) may be used torepresent the similarity between the voxels. The properties of thevoxels may be relatively similar if the distance is relatively close;the voxels may be relatively different if the distance is relativelyfar. If the spectral data are considered as vectors in amulti-dimensional space, the spectral angle may refer to the anglebetween the vector pixels or voxels and adjacent vector pixels orvoxels. The spectral angle of adjacent voxels may be used to measure thespectral difference between the adjacent voxels. If the spectral dataare considered as vectors in a multi-dimensional space, the spectraldistance may refer to the distance between the vector pixels or voxelsand adjacent vector pixels or voxels. The normalizing vector distancemay define the spectral difference between two voxels with acomprehensive consideration of the spectral angle and the spectraldistance; the smaller the spectral difference is, the higher thepossibility for the voxel to be selected as the seed point may be; thebigger the spectral difference is, the lower the possibility for thevoxel to be selected as the seed point may be.

In 1104, region growing of a first VOI may be performed based on the atleast one initial seed point. In some embodiments, operation 1104 may beperformed by the VOI generation unit 404. In some embodiments, regiongrowing of the first VOI may be performed based on one or more growingcriteria, each voxel in a neighborhood of an initial seed point may betraversed, and the region where the voxel(s) that meet the growingcriteria are located may be combined with the region where the initialseed point is located. In some embodiments, the newly added voxel(s) maybe used as the seed point(s) for a next round of region growing, and theregion growing may be continued until no pixel that satisfies thegrowing criteria may be found. In some embodiments, the growing criteriamay relate to characteristics of voxels, such as texture, gray level,colors, luminance, or the like, or any combination thereof. Forinstance, if the growing criteria relate to the gray level of voxels,the absolute value of the difference in the gray level values of theseed point and a neighborhood voxel is less than a pre-determinedthreshold, then the region where the voxel is located and the regionwhere the seed point is located may be combined. As another example, ifthe growing criteria relate to the similarity of texturecharacteristics, specifically, the mean value (e.g., contrast,correlation and entropy, etc.) of the texture characteristic values ofvoxels in the neighborhood of the seed point may be determined based ona gray level co-occurrence matrix, the mean value is compared with thetexture characteristic values of voxels in the region where the seedpoint is located, and the difference of the mean value of the texturecharacteristic values of voxels in the neighborhood of the seed pointand the texture characteristic values of voxels in the region where theseed point is located is less than a pre-determined threshold, then thevoxels in the neighborhood of the seed point may be combined into theregion where the seed point is located.

In some embodiments, the region growing may be performed based on imagedata. The image data may be volume data (e.g., a plurality oftwo-dimensional slice image sequences). The VOI grown based on imagedata may be used for the reconstruction and display of a VR image of theVOI region based on a volume rendering technique. For instance, avirtual projection line may pass through the VOI region oftwo-dimensional slice image sequence(s) at a pre-determined angle, atwo-dimensional projection may be performed on voxels in different sliceimages on the same projection line, then the voxels in different sliceimages may be comprehensively displayed based on a virtual illuminationeffect. In some embodiments, the region growing may be performed basedon a VR image. For instance, the initial seed point may be selectedbased on a VR image, the region growing may be performed, and then thegenerated VOI region may be displayed in different pseudo colors.

In 1106, a first texture of the first VOI may be drawn withoutconsidering depth information of an image. In some embodiments,operation 1106 may be performed by the VOI generation unit 404. In someembodiments, the VR image may include the first VOI and a backgroundregion. In some embodiments, the depth information may includethree-dimensional space information of voxels or pixels. The depthinformation may be used to represent the location of the voxels orpixels on the projection line that passes through the voxels or pixels,or the distance between the voxels or pixels and the projection plane.In some embodiments, in operation 1106, if a two-dimensional projectionof the three-dimensional coordinates of all the voxels in the first VOIis performed, a portion of the voxels on the projection line thatbelongs to the background region or have lower transparency than thevoxels in the first VOI may not be considered. In some embodiments, thefirst VOI may be a region including all the voxels extracted based onthe current region growing. In some embodiments, the first VOI may be aregion including newly added voxels extracted based on the currentregion growing that is performed based on the previous region growing.The first texture of the first VOI may be represented by the gray leveldistribution of the voxels in the VOI and neighborhood voxels thereof.The first texture of the first VOI may illustrate the spatial colordistribution and/or light intensity distribution of the voxels in thefirst VOI.

In some embodiments, the first texture of the first VOI may be drawnbased on one or more texture extraction techniques. The textureextraction techniques may include a statistics algorithm, a geometricalgorithm, a model algorithm, a signal processing algorithm, a structurealgorithm, or the like, or any combination thereof. The statisticsalgorithm may perform texture statistics in a region based on the graylevel characteristics of the voxels and the neighborhood voxels, such asa gray-level co-occurrence matrix (GLCM) algorithm, a gray-gradientco-occurrence matrix algorithm, swim-matrices statistics algorithm, agray level differential statistics algorithm, a crossed diagonal matrixalgorithm, a self-correlation function algorithm, a semi-variogramalgorithm, etc. The geometric algorithm may extract the texture based onthat the texture of the voxels if formed by arranging a plurality ofvoxels under a certain rule, such as a Voronio checkerboardcharacteristics algorithm. The model algorithm may extract the texturebased on that the texture of the voxels is formed by a certaindistribution model, wherein the distribution model may be controlled byparameters. An exemplary model algorithm may include a random modelalgorithm, a fractal model algorithm, a complex network model algorithm,a mosaic model algorithm, etc. A typical model algorithm may include arandom model algorithm, such as a Markov Random Field (MRF) modelalgorithm, a Gibbs random model algorithm, a moving average modelalgorithm, a simultaneous autoregressive model algorithm, anautoregressive sliding model algorithm, a generalized correlation modelalgorithm, etc. Based on a spatial domain, a transformation domainand/or a multi-scale analysis, the signal processing algorithm mayperform a correlation transformation on a region of the image and thenextract relatively stable characteristic values, and represent theconsistency in the region and the difference between regions using thecharacteristic values. The signal processing algorithm may be performedbased on a transformation algorithm, a filtering algorithm, a Lawstexture measurement algorithm, or the like, or any combination thereof.The transformation algorithm may include a Radom transformationalgorithm, a local Fourier transformation algorithm, a local Walshtransformation algorithm, a Gabor transformation algorithm, a wavelettransformation algorithm, a hadamard transformation algorithm, adiscrete cosine transformation algorithm, etc. The filtering algorithmmay include a characteristic filtering algorithm, a quadrature mirrorfiltering algorithm, an optimized finite impulse response (FIR)filtering algorithm, etc. The structure algorithm may extract texturecharacteristics based on the type and number of the texture elements,the repetitive spatial organization structure among the elements, and/orthe arrangement rule, such as a syntax texture analysis algorithm, amathematical morphology algorithm, etc.

In 1108, the region growing of the first VOI may be suspended. In someembodiments, operation 1108 may be performed by the VOI generation unit404. In some embodiments, a user may input an instruction for suspendingthe region growing via the interactive device 140 (e.g., a mouse, akeyboard, etc.), and the VOI generation unit 404 may receive theinstruction and suspend the region growing. In some embodiments, theregion growing may be suspended by interactive operations such asrotating, translating, and/or scaling the operation interface, etc. Insome embodiments, the region growing may be suspended by an operation ofreleasing the mouse.

In 1110, a second texture of a second VOI may be drawn based on depthinformation of the image. In some embodiments, operation 1110 may beperformed by the VOI generation unit 404. In some embodiments, the depthinformation of voxels may be extracted based on image data to obtain thedepth information of the voxels in operation 1110. The extraction of thedepth information may be performed based on one or more depthinformation extraction techniques. The depth information extractiontechnique(s) may include multi-view stereo algorithm, a photometricstereo vision algorithm, a defocusing inference algorithm, an algorithmbased on machine learning, or the like, or any combination thereof. Themulti-view stereo algorithm may extract pixels based on two-dimensionalimages, match the voxels with angle images thereof, and then determinethree-dimensional coordinates of the voxels based on the matched voxels.The photometric stereo vision algorithm may estimate one or more VOIsurface normal vectors based on image sequence(s) under differentillumination conditions, obtain the three-dimensional coordinates of thefinal voxels using techniques such as a line integral algorithm, andobtain depth information of the voxels. The defocusing inferencealgorithm may reckon the depth information of voxels based on the fuzzyextent of the VOI. The algorithm based on machine learning may includeusing a Markov Random Field model as a model for machine learning, andperforming supervised learning. In some embodiments, the drawing of thesecond texture of the second VOI may be performed according to one ormore texture extraction algorithms described in the present disclosure.

In some embodiments, the first VOI may include a first voxel set, andthe second VOI may include a second voxel set. In some embodiments, thefirst voxel set may include the second voxel set. In some embodiments,the second VOI may be determined based on the depth information ofvoxels in the first voxel set and/or the transparency of voxels in a VRimage. For example, a projection line passing through one voxel of thefirst voxel set may be used to pass the VR image at a pre-determinedangle. On the projection line between the voxel and the projectionplane, if no other voxel that has higher transparency than the voxel ispresent, then the voxel may belong to the second VOI region; if anothervoxel that has higher transparency than the voxel is present on theprojection line between the voxel and the projection plane, then thevoxel may not belong to the second VOI region.

In 1112, the VR image may be blended with the first texture of the firstVOI or the second texture of the second VOI. In some embodiments,operation 1112 may be performed by the VOI generation unit 404. In someembodiments, after the VR image and the first texture of the first VOIare blended, the first VOI may be displayed in the VR image in operation1112. The first VOI may include voxels newly added during the process ofregion growing (i.e., the voxels newly added in the current regiongrowing compared with previous region growing) or all the voxelsgenerated during the process from the original region growing to thecurrent region growing. In some embodiments, after the VR image and thesecond texture of the second VOI are blended, the second VOI may bedisplayed in the VR image in operation 1112. For instance, during theextraction process of blood vessel tissue of head and neck, the bloodvessel tissue may be the first VOI, a portion of the blood vessel tissuethat is not masked by the skull may be the second VOI. The first VOI andthe VR image of head and neck may be blended for presenting the changingprocess of the blood vessel tissue (e.g., whether the growing of bloodvessel tissue is overflowed, incomplete or not). The second VOI and theVR image of the head and neck may be blended for presenting theshadowing relationship of the blood vessel tissue and tissue such as theskull, etc.

It should be noted that the above descriptions of the process 1100 areonly for the convenience of illustration, and not intended to limit thepresent disclosure to the scope of the exemplary embodiments. It shouldbe understand that for those skilled in the art, after understanding theprinciples of the present disclosure, various modifications may beconducted to the process 1100 without departing from the principles. Forexample, the process 1100 may further include pre-processing the imagedata. As another example, operation 1108 and/or 1110 may be omitted fromthe process 1100. As a further example, the growth status of regiongrowing may be displayed in real time in a VR display window and/or anMPR display window. Similar variations fall within the protection scopeof the present disclosure.

FIG. 12 is a flowchart of an exemplary process for non-linear VOI regiongrowing according to some embodiments of the present disclosure. In someembodiments, the operation 1104 of performing region growing on thefirst VOI in the process 1100 may be implemented according to theprocess 1200 as illustrated in FIG. 12. In operation 1202, regiongrowing may be started. In some embodiments, operation 1202 may beimplemented by the VOI generation unit 404. In some embodiments, a usermay input an instruction of starting the region growing via theinteractive device 140, and the VOI generation unit 404 may initiate theregion growing based on the instruction inputted by the user. Forexample, a user may click on the image region using a mouse to initiate(or start) the region growing. As another example, a user may click aninitiation button or a starting button in the operation interface toinitiate (or start) the region growing.

In 1204, a timer may be started. The operation 1204 may be performed bythe VOI generation unit 404. In some embodiments, the VOI generationunit 404 may start the timer at the time when the region growing isstarted or after a time interval. In some embodiments, the timer may bestarted at the time when the region growing is started in the operation1202. For instance, when a user clicks on an image region and starts theregion growing, the timer may be simultaneously started. In someembodiments, after the region growing is started, the timer may beautomatically started after a pre-determined first time interval. Thefirst time interval may be pre-determined based on the size of the VOIregion. For instance, if the VOI region is relatively small, then thefirst time interval may be relatively short; if the VOI region isrelatively large, then the first time interval may be relatively long.

In 1208, whether to stop region growing may be determined. In someembodiments, operation 1208 may be performed by the judgment unit 414.In some embodiments, the stop of the region growing may include atermination or a suspension of the region growing. In some embodiments,whether a condition is satisfied for terminating the region growing ornot may be determined in 1208. The condition for terminating the regiongrowing may include terminating the region growing if no further voxelsatisfies the growing criteria. More descriptions of the growingcriteria may be found elsewhere in the present disclosure (e.g., FIG. 11and descriptions thereof). In some embodiments, whether to suspend theregion growing or not may be determined based on user interactiveinformation in 1208. For instance, the region growing may be suspendedif a user performs an operation such as releasing a mouse and/ortranslating, rotating, or scaling the operation interface, etc. If theregion growing is not stopped, an operation 1210 of extracting seedpoints may be performed; if the region growing is stopped, an operation1206 of stopping the timer may be performed. In some embodiments, thetimer may record the frequency of extraction of seed points. Forexample, the times of the operation of the timer may represent the timesof the extraction of the seed points.

In 1210, the times of extracting the seed points may be determined basedon the timer. In some embodiments, the extraction times of the seedpoints may be determined in a pre-determined second time interval afterthe timer is started. The second time interval may be set based on thesize of the VOI region. For instance, if the VOI region is relativelysmall, then the second time interval may be relatively short; if the VOIregion is relatively large, then the second time interval may berelatively long.

In 1212, whether the number of the extraction times of the seed pointsis less than or equal to a pre-determined threshold may be determined.If the number of the extraction times of the seed points is less than orequal to the pre-determined threshold, then 1214 may be performed; ifthe number of the extraction times of the seed points is greater thanthe pre-determined threshold, then 1216 may be performed. In someembodiments, the pre-determined threshold may be set based on the sizeof the VOI region. For instance, if the VOI region is relatively small,then the pre-determined threshold may be relatively small; if the VOIregion is relatively large, then the pre-determined threshold may berelatively large. In some embodiments, the pre-determined threshold mayrange from 1 to 5. For example, the pre-determined threshold may be 5.In some embodiments, the pre-determined threshold may range from 1 to10. It should be noted that, the above mentioned values for thepre-determined threshold are only provided for the convenience ofdescription, and may not limit the present disclosure to the scope ofthe embodiments.

In 1214, the region growing may be performed based on the seed pointswith a relatively low speed. In some embodiments, in the operation 1214,the growing speed of the seed points may be decreased during the timeinterval of the current extraction of seed points and the nextextraction of seed points, so as to decrease the number of the grownseed points. For instance, if the VOI region is relatively small, a usermay desire to observe the status of the VOI region growing, and then asmall number of seed points may be grown with a relatively low speed tocontrol the VOI region and prevent the seed points from growing toofast.

In 1216, the region growing may be performed based on the seed pointswith a relatively high speed, and the speed may be increasing until thenumber of the seed points tends to be stable. In some embodiments, inthe operation 1216, the growing speed of the seed points may beincreased during the time interval of the current extraction of seedpoints and the next extraction of seed points, so as to increase thenumber of the grown seed points. For instance, if the VOI region isrelatively large, a user may desire that the speed of the region growingis relatively high during the early stage of the VOI region growing;however, during the late stage of the VOI region growing, the growingmay easily overflow due to the large number of seed points, and thus thespeed of the region growing may need to be decreased. The growing speedmay be adjusted based on the operation times of the timer determined in1210. Therefore, in the early stage of region growing, the growing speedof the seed points may be increased to increase the amount of the grownseed points, so as to increase the speed of region growing. In the latestage of the region growing, the growing speed of the seed points may bedecreased to make the amount of the grown seed points tend to be stable,so as to decrease the speed of region growing. It should be noted that alow speed and a high speed is relative. For instance, a speed lower thana pre-determined value may be considered as a relatively low speed,while a speed higher than the pre-determined value may be considered asa relatively high speed. Similarly, a small number of seed points and alarge number of seed points may also be relative. For example, a numberof seed points less than a pre-determined number may be considered as asmall number of seed points, while a number of seed points greater thanthe pre-determined number may be considered as a large number of seedpoints.

It should be noted that the above descriptions of the process 1200 fornon-linear VOI region growing are only for the convenience ofillustration, and not intended to limit the present disclosure to thescope of the exemplary embodiments. It should be understand that forthose skilled in the art, after understanding the principles of thepresent disclosure, various modifications may be conducted to theprocess 1200 for non-linear VOI region growing without departing fromthe principles. For example, the process 1200 for non-linear VOI regiongrowing may include selecting initial seed points. As another example,the process 1200 for non-linear VOI region growing may includesuspending region growing, rending image(s), displaying image(s), etc.As a further example, the status of region growing may be displayed inreal time in a VR display window or an MPR display window. Similarvariations fall within the protection scope of the present disclosure.

FIG. 13 is a flowchart of an exemplary process for determining a VOIbased on a VR image according to some embodiments of the presentdisclosure. The process 1300 may be an exemplary embodiment of theprocess 1100.

In 1302, a VR image may be acquired. In some embodiments, operation 1302may be performed by the image data acquisition module 302. In someembodiments, the VR image may be acquired by reconstructing the VR imagebased on image data using a volume rendering technique. In someembodiments, the VR image may include at least one VOI and/or backgroundregion.

In 1304, region growing of a VOI may be performed. In some embodiments,operation 1304 may be performed by the VOI generation unit 404. In someembodiments, the determination of the VOI may include a plurality ofprocesses of region growing, and similar or the same voxels may beextracted in each process of region growing. In some embodiments, theregion growing may include selecting an initial seed point. The initialseed point may be selected manually or automatically. More descriptionsof the selection of the initial seed point may be found elsewhere in thepresent disclosure (e.g., FIG. 11 and descriptions thereof). The initialseed point may be selected based on voxel texture characteristics. Insome embodiments, neighborhood voxels of the initial seed point may betraversed to select voxels that satisfy pre-determined growing criteriain 1304. The voxels that satisfy the pre-determined growing criteria maybe combined with the initial seed point into a same region, i.e., theVOI region. Newly added voxels in the VOI may serve as new seed pointsfor next region growing (i.e., extracting similar or the same voxels inthe next growing).

In 1306, whether to stop region growing may be determined. In someembodiments, operation 1306 may be performed by the VOI generation unit404. In some embodiments, a current operation mode may be determined,and whether to stop region growing may be determined based on thecurrent operation mode in 1306. In some embodiments, the currentoperation mode may include the operational action by a user, forinstance, clicking a mouse, releasing a mouse, translating, rotating, orscaling the operation interface, etc. In some embodiments, if the userperforms an operational action such as clicking a mouse, inputting aninstruction of starting or keeping a mouse as unreleased, etc., 1308 maybe performed.

In 1308, a first set of seed points may be determined. In someembodiments, operation 1308 may be performed by the VOI generation unit404. The first set of seed points may include at least one of firstvoxels. The first voxels may include at least one voxel newly added inthe process of the current region growing relative to the process of theprevious region growing. For instance, the VOI generation unit 404 maystart (or resume after a previous growing suspension) performing a VOIregion growing at a first time point, and suspend the VOI region growingat a second time point. Thus the first set of seed points may includethe seed points growing from the first time point to the second timepoint. In some embodiments, the newly added voxels may represent areal-time changing status of the VOI growing. In some embodiments, thecurrent region growing may include a process of generating/extractingseed points at a time closest to but before the time point of suspendingthe region growing.

In 1310, two-dimensional projection coordinates of the first set of seedpoints may be determined based on three-dimensional coordinates of thefirst set of seed points, without considering depth information. In someembodiments, operation 1310 may be performed by the VOI generation unit404. In some embodiments, a projection line may pass through the firstvoxels at a certain angle, and the first voxels may project on aprojection plane (i.e., a display plane) to generate two-dimensionalprojection coordinates. In some embodiments, when a two-dimensionalprojection is performed on the first voxels, voxels in the backgroundregion on the projection line passing from the first voxels to theprojection plane, or voxels that have a lower transparency value thanthe first voxels may not be considered. In some embodiments, atransparency value lower than the transparency value of the first voxelsmay be set as a maximum value (or the opaqueness may be set as 0). Forexample, if region growing is performed on blood vessel tissue of headand neck based on volume rendering (VR) images of head and neck, aportion of the blood vessel tissue may be masked by the skull. The firstset of seed points may be blood vessel tissue growing in real time. Ifthe depth information of the VR image of the head and neck is notconsidered, then the transparency of an image of the skull portion maybe set as the maximum value. In other words, the growing status of theblood vessels may be displayed without considering the shadowingrelationship of the blood vessel tissue and the skull.

In 1312, the first texture of the first set of seed points may bedetermined based on the first set of seed points and the two-dimensionalprojection coordinates. In some embodiments, operation 1312 may beperformed by the VOI generation unit 404. In some embodiments, the firsttexture may be the texture of first voxels in the first set of seedpoints. In some embodiments, the texture of the first voxels within aregion corresponding to the two-dimensional projection coordinates maybe determined based on the two-dimensional projection coordinates. Insome embodiments, the extraction of the texture characteristics may beperformed based on one or more texture extraction techniques describedin the present disclosure. In some embodiments, a texture extractionoperation may be performed on the first set of seed points, and theextracted texture may be drawn and displayed in the corresponding regionof the two-dimensional projection coordinates.

In 1314, the first texture and the VR image may be blended. In someembodiments, operation 1314 may be performed by the VOI generation unit404. In some embodiments, the texture of the background region and thefirst texture may be blended in 1314. The texture of the backgroundregion may be extracted based on any texture extraction techniquedescribed in the present disclosure.

In 1318, a second set of seed points may be determined. In someembodiments, operation 1318 may be performed by the VOI generation unit404. The second set of seed points may include at least one of secondvoxels. The second voxels may include all the voxels newly added fromthe beginning of the region growing to the suspension time point of theregion growing. In some embodiments, the first set of seed points mayinclude the second seed points.

In 1320, depth information of a currently displayed VR image may bedetermined. In some embodiments, operation 1320 may be performed by theVOI generation unit 404. In some embodiments, the currently displayed VRimage may include a portion of the grown VOI. More descriptions of thedetermination of the depth information may be found elsewhere in thepresent disclosure (e.g., FIG. 11 and descriptions thereof). In someembodiments, the depth information of the VR image may include the depthinformation of voxels in the current VR image. In some embodiments, thedepth information of voxels in the currently displayed VR image may bedetermined based on the current VR image in 1320. In some embodiments,the depth information of the voxels may be determined based on imagedata (e.g., a two-dimensional slice image sequence) in 1320.

In 1322, a third set of seed points may be determined based on the depthinformation. In some embodiments, operation 1322 may be performed by theVOI generation unit 404. The third set of seed points may include atleast one of third voxels. In some embodiments, the third set of seedpoints may include at least a portion of the second set of seed points.For example, the VOI generation unit 404 may start to perform (or resumeafter a previous suspension) the region growing of the VOI at a firsttime point, suspend the region growing of the VOI at a second timepoint, and continue the region growing of the VOI at a third time point.Thus the third set of seed points may include at least a portion of theseed points grown from the first time point till the third time point.In some embodiments, the depth information may represent locationinformation of different voxels in the three-dimensional space. In someembodiments, the third voxels and the third set of seed points may bedetermined based on the location information in the three-dimensionalspace and the transparency of different voxels. For example, aprojection line may be used to pass through the VR image (e.g., thecurrently displayed VR image or the VR image acquired in 1302) at apre-determined angle, and the projection line may pass through thesecond voxels in the second set of seed points. On the projection linefrom the location of a second voxel to the projection plane, if no voxelthat has lower transparency than the second voxel is present, then thesecond voxel may be a third voxel in the third set of seed points. If avoxel that has lower transparency than the second voxel is present, thenthe second voxel may not be a third voxel in the third set of seedpoints.

In 1324, two-dimensional projection coordinates of the third set of seedpoints may be determined based on the three-dimensional coordinatesthereof. In some embodiments, operation 1324 may be performed by the VOIgeneration unit 404. In some embodiments, a projection line may passthrough the third voxel in the third set of seed points at apre-determined angle, and the third voxel may be projected onto aprojection plane (i.e., a display plane) and generate two-dimensionalprojection coordinates.

In 1326, a second texture of the third set of seed points may bedetermined based on the third set of seed points and the two-dimensionalprojection coordinates. In some embodiments, operation 1326 may beperformed by the VOI generation unit 404. In some embodiments, thesecond texture may be the texture of the third voxels in the third setof seed points in the region corresponding to the two-dimensionalcoordinates. In some embodiments, the second texture may be extractedbased on any texture extraction technique described in the presentdisclosure. In some embodiments, a texture extraction operation may beperformed on the third set of seed points, and the extracted texture maybe drawn and displayed in the corresponding region of thetwo-dimensional projection coordinates.

In 1328, the VR image and the second texture may be blended. In someembodiments, operation 1328 may be performed by the VOI generation unit404. In some embodiments, the texture of the background region may beblended with the second texture. The texture of the background regionmay be extracted based on any texture extraction technique in thepresent disclosure.

It should be noted that the above descriptions of the process 1300 areonly for the convenience of illustration, and not intended to limit thepresent disclosure to the scope of the exemplary embodiments. It shouldbe understand that for those skilled in the art, after understanding theprinciples of the present disclosure, various modifications may beconducted to the process 1300 without departing from the principles. Forexample, the process 1300 may include acquiring image data. As anotherexample, the process 1300 may further include pre-processing the imagedata. Similar variations fall within the protection scope of the presentdisclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art that the foregoing detailed disclosure isintended to be presented by way of example only and is not limiting.Various alterations, improvements, and modifications may occur and areintended to those skilled in the art, though not expressly statedherein. These alterations, improvements, and modifications are intendedto be suggested by this disclosure, and are within the spirit and scopeof the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with at least oneembodiment of the present disclosure. Therefore, it is emphasized andshould be appreciated that two or more references to “an embodiment” or“one embodiment” or “an alternative embodiment” in various portions ofthis specification are not necessarily all referring to the sameembodiment. Furthermore, the particular features, structures orcharacteristics may be combined as suitable in one or more embodimentsof the present disclosure.

Furthermore, it will be appreciated by one skilled in the art, aspectsof the present disclosure may be illustrated and described herein in anyof a number of patentable classes or context including any new anduseful process, machine, manufacture, or composition of matter, or anynew and useful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely by hardware, entirely by software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Moreover, aspects of the present disclosure may take the formof a computer program product embodied in one or more computer readablemedia having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the operator's computer, partly on the operator's computer,as a stand-alone software package, partly on the operator's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe operator's computer through any type of network, including a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of thedisclosure are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate,” or “substantially” may indicate ±20% variationof the value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothe embodiments precisely shown and described in the presentapplication.

1. A method implemented on at least one machine, each of the at leastone machine having at least one processor and one storage, the methodcomprising: acquiring image data in a first sectional plane, the imagedata in the first sectional plane including at least one first sliceimage and one second slice image; determining a first region of interest(ROI) in the first slice image; determining a second ROI in the secondslice image; and determining, based on the first ROI, the second ROI,and characteristic information of the image data in the first sectionalplane, a first volume of interest (VOI).
 2. The method of claim 1,wherein the image data in the first sectional plane include image datain a transverse plane, image data in a coronal plane, or image data in asagittal plane.
 3. The method of claim 1, further comprising: displayingthe first ROI or the second ROI in a two-dimensional reconstructionview, and displaying the first VOI synchronously in a three-dimensionalreconstruction view; and displaying the first ROI or the second ROI inthe first VOI displayed in the three-dimensional reconstruction view. 4.The method of claim 1, wherein the determining a first VOI comprises:determining, based on the characteristic information, whether the firstROI, the second ROI, or the first VOI satisfies a pre-determinedcondition; in response to a determination that the first ROI, the secondROI, or the first VOI does not satisfy the pre-determined condition:editing the first ROI or the second ROI; and generating, based on theedited first ROI or the edited second ROI, an edited first VOI.
 5. Themethod of claim 1, further comprising: determining a first contour lineof the first ROI, the first contour line including at least one firstcontrol point; determining a second contour line of the second ROI, thesecond contour line including at least one second control point;displaying the first contour line or the second contour line in thefirst VOI; and editing the at least one first control point of the firstcontour line or the at least one second control point of the secondcontour line in the first VOI to obtain an edited first ROI or an editedsecond ROI, and an edited first VOI.
 6. The method of claim 4, whereinthe pre-determined condition relates to whether the first ROI, thesecond ROI, or the first VOI includes at least one of a blood vessel,calcified tissue, or fracture tissue.
 7. The method of claim 1, furthercomprising: generating a first curve in the first slice image, whereinthe first curve includes at least one first control point, and the firstcurve divides the first ROI into at least two regions; and generating asecond curve in the second slice image, wherein the second curveincludes at least one second control point, and the second curve dividesthe second ROI into at least two regions.
 8. The method of claim 7,further comprising: generating, based on the at least one first controlpoint of the first curve and the at least one second control point ofthe second curve, a first curved surface using an interpolationalgorithm, the first curved surface dividing the first VOI into at leasttwo portions.
 9. The method of claim 8, further comprising: displayingthe first curve or the second curve in a multiplanar reconstructionwindow; and synchronously displaying the first curved surface, the firstcurve, or the second curve in a volume rendering window.
 10. The methodof claim 1, further comprising: optimizing, based on the characteristicinformation of the image data, the first VOI to obtain a second VOI, thesecond VOI including at least one portion of the first VOI.
 11. Themethod of claim 1, wherein the first VOI includes a third VOI, and themethod further comprises: performing, based on the first VOI, regiongrowing of the third VOI at a first point in time; suspending regiongrowing of the third VOI at a second point in time; determining, basedon depth information of the image data and the first VOI, at least oneportion of the third VOI, wherein the at least one portion of the thirdVOI includes at least one first voxel, and a depth relating to the firstvoxel is less than or equal to a depth relating to the image data;determining, based on the at least one portion of the third VOI, a firsttexture, the first texture including gray level distribution informationof the at least one first voxel; and determining, based on the firsttexture and the first VOI, a second texture, the second textureincluding the first texture.
 12. The method of claim 1, wherein thecharacteristic information includes gray level information. 13-28.(canceled)
 29. The method of claim 8, further comprising: editing, basedon the first curved surface, the first curve or the second curve; andgenerating, based on the edited first curve or the edited second curve,a second curved surface.
 30. The method of claim 29, wherein the editingthe first curve or the second curve comprises: adjusting the at leastone first control point of the first curve and/or the at least onesecond control point of the second curve based on the first curvedsurface, adjusting the at least one first control point of the firstcurve based on the first curve, or adjusting the at least one secondcontrol point of the second curve based on the second curve.
 31. Themethod of claim 9, further comprising: adjusting, based on the firstcurved surface, the at least one first control point of the first curveor the at least one second control point of the second curve in thevolume rendering window or a mesh rendering window.
 32. The method ofclaim 11, wherein the determining at least one portion of the third VOIcomprises: determining a first set of seed points, the first set of seedpoints including all seed points growing from the first point in time tothe second point in time; determining a second set of seed points,wherein the first set of seed points including the second set of seedpoints, and wherein a depth relating to the second set of seed points isless than or equal to the depth relating to the image data; determining,based on a plurality of three-dimensional coordinates of the second setof seed points, a plurality of two-dimensional projection coordinates ofthe second set of seed points; and determining, based on the pluralityof two-dimensional projection coordinates of the second set of seedpoints, the at least one portion of the third VOI.
 33. The method ofclaim 11, further comprising generating a third texture of the first VOIwithout considering the depth information of the image data, whichcomprises: determining a third set of seed points at a third point intime, the third set of seed points including at least one portion of aplurality of seed points growing from the first point in time to thethird point in time; determining, based on a plurality ofthree-dimensional coordinates of the third set of seed points, aplurality of two-dimensional projection coordinates of the third set ofseed points; and determining, based on the plurality of two-dimensionalprojection coordinates of the third set of seed points, the thirdtexture of the first VOI, the third texture including gray leveldistribution information of at least one voxel of the first VOI.
 34. Themethod of claim 11, wherein the region growing of the third VOIcomprises: determining a number of extraction times of a plurality ofseed points during the region growing from the first point in time tothe fourth point in time; determining whether the number of extractiontimes of the plurality of seed points is less than or equal to apre-determined value; in response to a determination that the number ofextraction times of the plurality of seed points is less than or equalto the pre-determined value, decreasing a speed of generating aplurality of new seed points; and in response to a determination thatthe number of extraction times of the plurality of seed points is morethan the pre-determined value, increasing the speed of generating theplurality of new seed points.
 35. A non-transitory computer readablemedium comprising executable instructions that, when executed by atleast one processor, cause the at least one processor to effectuate amethod comprising: acquiring image data in a first sectional plane, theimage data in the first sectional plane including at least one firstslice image and one second slice image; determining a first region ofinterest (ROI) in the first slice image; determining a second ROI in thesecond slice image; and determining, based on the first ROI, the secondROI, and characteristic information of the image data in the firstsectional plane, a first volume of interest (VOI).
 36. A systemcomprising: at least one processor, and a storage configured to storeinstructions, the instructions, when executed by the at least oneprocessor, causing the system to effectuate a method comprising:acquiring image data in a first sectional plane, the image data in thefirst sectional plane including at least one first slice image and onesecond slice image; determining a first region of interest (ROI) in thefirst slice image; determining a second ROI in the second slice image;and determining, based on the first ROI, the second ROI, andcharacteristic information of the image data in the first sectionalplane, a first volume of interest (VOI).